Where business and data science meet: Interview with BCG Data Scientist

hey this is daniel huss ceo of gravity ai and in today's interview we'll be talking with mehdi salmani who's a lead data scientist at boston consulting group's digital ventures team as part of our ongoing series that connects business with data science take a listen maddie uh thank you so much uh for taking the time today to talk with us my pleasure um as you know we're exploring the intersection of data science and the business world and how those two communicate and work together um so i kind of want to jump right in with the first question um which is what is one thing that you wish that the executives the business side of the world knew or understood better um about data science yeah uh if you don't mind i will break down my answer to probably two three things uh the first thing is i would suggest executives to learn the very basics about ai and machine learning in general it might take them five minutes 10 minutes to just understand the paradigm and the ai paradigm and machine learning how it works you know and then that will help them to gauge the ideas out there or the ideas that they have people are coming to them is it feasible or not and the second part would be that they should not expect what people are going to do in data science it's kind of like science fiction movies or chinese propaganda videos that they see they can track people in a few seconds where you are these kind of things uh it it creates a huge uh like uh expectation unrealistic expectations yeah and unrealistic expectations um leads to uh huge disappointments in business you know if they understand hey these are the risks these are the power of ai machine learning that can help them empower their business that will be very helpful for them you know it's funny to say that i think you know you've seen so much in the movies that has painted this picture of where this is going yeah um that sometimes you have you know i sometimes call it like the shiny object syndrome where you know i remember when uh you know back in design when minority report came out everyone wants the the hand controlled things you know now we're starting to see that in the world of data science yeah what do you think uh have you seen any uh examples in like movies that you can think off the top of your head where like this has actually come to fruition as as you're like uh okay this is close to reality or there are a lot of things going on in black mirror oh yeah that some of them are fairly realistic sure and the way that they made the movie a lot of a lot of episodes there you can think of that might happen in the near future in long sure sure i think that black mirror is probably the best sample for ai in general yeah and they are they have a lot of i don't remember exactly the title of the episode but i've seen multiple episodes there which is uh it's pretty striking you know sometimes maybe i can do that today too you know i might be able to do that yeah but it's not real yeah uh so um uh i very much agree to with your point about um you know from an executive standpoint the business world does have that kind of movie view and obviously there's a lot of nomenclature around machine learning and ai um what uh can you think of any good resources or where where should an executive start do you think like to just start grasping some of this um and the differences between you know what's possible and what's yeah there are i believe there are courses on coursera like very short courses for executives design for executives or linkedin learning or i'm sure like bigger organizations they have data science teams yeah just sit down with one of the people who have some understanding also yes business to explain for them for 10 minutes yeah or maybe half an hour and they can explain for them what's ai machine learning and what it can do for them right right now and that's probably the i would start and also hbr uh harvard business review has multiple articles around in data space it can be very helpful too yeah those are great um all right so let's do the flip side of that question now like so uh uh if you're an executive right so um obviously they have certain goals that they're trying to achieve um what's one thing that you imagine that they wish data scientists knew about their side of their business i would say probably the most important thing is that business people they are looking for impact yeah a lot of time the dollar amount next to that yeah yeah on the data science side especially if there are phds coming out of quantitative fields and what they want to do or what we want to do is doing some cool stuff we may not necessarily care about kpis that we have to yeah yeah we don't care about the impacts at the end of the time or the execution time you know and another thing i found that it's very interesting for usually business leaders who they want to see is the risks uh which are coming with these type of systems especially they are very complex models yeah and if they are going to replace that what are the risks around it and data scientists if they can explain that elaborate that hey these are the risks these are the limitations of the models systems that we are building which it can help business leaders to think and decide better could we maybe elaborate just a little bit more on that because i think that's a really good point and you know i have an example in my own head of of an instance of that but could you um maybe talk a little bit more about some of the types of risks that you see that are associated yeah with building models and how they relate back to that kind of business function and things that like up and coming data scientists should start to become aware of yeah sometimes that you ha generally your machine learning models uh might be limited to the range of the data it has seen yeah and when it's go to an unseen range of the data not unseen data like outside of the range of the data it has seen before right you know imagine that you are a ride-sharing company and you have your own pricing the data for your pricing for just la area and if you have a request in santa barbara what is your machine system is going to do you have to be careful about that yeah you know that's the limitation when you are building the model you have provided a lot of data for la area yeah if it goes outside of that it might mess up unless that the data scientist or the whole system design team they understand hey these are the risks and they are mitigating it yeah they have to make sure that they are going to see these kind of uh issues yeah and sometimes it's it needed to be communicated with the business team i think that's um really interesting i've had an experience where sometimes machine learning or models are viewed as like almost binary it works or it doesn't work yeah and that's not the case right like there's a range of possibilities here yeah how how have you seen effective ways for teams to kind of communicate that out um and maybe what are the right questions that executives should be asking i believe that these two questions what are the risks of the this model okay that's hey you are coming up with this demand forecast for me sure how confident are you there yeah you know and it's good always to uh do some fact checking you know you are going to provide the data to the data science team consulting team sometimes keep a little bit of data for your own sake to check it later with their model their output yeah and finding out what is the confidence level helps you to make the decision much more informed decision yeah that's on the risk and the also on the limitation it's good to know hey this is the limitation we are not going to abuse the system or misuse the system for other things yeah and and when you say confidence level yeah um you know that's literally translating into a percentage right like um you know is is is this working 70 of the time uh confidence level and accuracy would be a bit different different yeah okay accuracy is the kpis and metrics that you are going to look at that i see but the confidence is uh sometimes that that is in demand forecasting for example we come up with some demands for this specific company for the logistics right like we are going to expect to sell 10 000 pieces of x right next month how much confidence are you that that forecast yeah what if i i'm going to have 15 000 uh requests you know yeah if i don't have the confidence level around that right not to be prepared i might lose a customer yeah there are real business implications for having you know so they can make a better informed decision you know if you come in and you say i'm ten percent confident and yeah you know like oh right exactly and you might sometimes for some businesses you might need 95 percent obviously sometimes 80 it's enough you know to make the decision yeah yeah they can work in a buffer exactly but it's important for the uh business leaders to understand that so uh on the accuracy side again as well um there's important decisions to make there too so like not necessarily forecasting but like there could be user experience implications and other business implications yeah any anything come to mind on that side that you've encountered as far as a risk concern an accuracy side uh it's important to understand the different types of errors and mistakes that the error system might make imagine that you have an uh you're building an autonomous vehicle it sees an object next to the road if that's a human yeah or not it can be detrimental yeah you know it can be very crucial to understand if there is a like a high chance of having this as a human probably you need to slow down or even stop yeah you know if you make a mistake a human with an object that can probably kill a program right or a project right the consequences are pretty severe you should understand but if you make a mistake between object and detect it as a human probably it's less important you know if you stop yeah there will be some unsatisfaction for the driver but it's much less important compared to hitting a person right you know so that's you know a direct business decision right um discomfort of you know perhaps stopping too much or too early yes versus the trade-off of a substantially larger issue exactly um and those things need to be communicated false positive and false negatives and false negatives that you have to identify and the value assigned to each of them yeah yeah do you um so on that do you think and i'm just guessing here do you think too often um the business side is relying on the data scientists to just make those decisions in a vacuum or do you think that they have a good understanding of how those might actually come into the business if companies they started just just started their own data science division or working on the decent projects they may not know they have to communicate these things with data science team and if the data science team is not very experienced they may not find that out soon enough you know and these are the things probably needed to be communicated from the beginning hey these are the important factors for us these are the kpis that we want to see these are the dollar amount assigned to each kind of mistakes that you might make yeah you know yeah and based on that if they define well-defined that from the beginning will be much better for the data scientists and also for the business leaders so getting getting the the business leaders to really kind of map out their priorities right like the most important aspects of this and for them what they see are the key risks of you know the product or project that they're working on and generally business leaders they understand the dollar amount yeah you know they they can like come aboard the business people can come up with the numbers for each kind of mistakes which whatever the errors that the system might have yeah so actually on that subject um i've seen a couple numbers uh that suggest that nearly nine out of ten internal data science projects fail um could could you give us your take uh on why you think that is and um there's probably a multitude of things that impact that um so let's talk about some of those and then maybe talk about some of the we've touched on it but the business implications of that as well uh i'm a data person and i love to see that how they did the how they came up with that number yeah i want to see the sample size where they are but if i want to guess probably [Music] i'll put a a credit right here we'll link to the article uh i would uh i would probably see that one of the main issues can uh come from the miscommunication yeah if you don't communicate well what you want from data science or you have very high or unrealistic expectations yeah that might fail yeah and sometimes all back to the movies yes exactly and there are other issues like lack of data yeah proper data and sometimes also on the data science side they might underestimate the amount of time they they need or what they are going to deliver ambiguity how you are going to define a project how your what you are going to deliver what are the kpis that you are going to meet these are the things that probably at the beginning needs to be uh they needed to be uh well defined before even they start projects yeah um now in in regards to that uh you mentioned the time that it's going to take in your experience like how long does it create time uh to create a model right um just to get the model up and running and then there's obviously optimization time after that what types of ranges have you seen that on the business side they they might be surprised to hear yeah there are when you want to get a model just up and running and test you generally need to prepare your data first and have the data ready to do your analysis on that uh that might take you it depends if the data is ready or not i assume that your data is ready if the data is ready uh running a model building a model problem for the initial model probably might take you a day or two nice yeah and you can get an initial idea what's going on there yeah yeah this would be the to understand the feasibility of the project right like how much additional effort will it take yeah yeah yeah from model to come up to an insight or turn that to a product it's a much much longer way yeah i remember i built a uh i was building a product a quarter vision product for one of the companies they were assigning around six months for that project and after first day i built an initial model yeah like they were saying oh if you can do that in a day maybe what you can do six months i said no no wait wait wait this is just a model turning that to a product might take us the next three to six months and it took us around three months to turn that to a product yeah you know which their users uh the people could use it like chipotle and get what they wanted yeah yeah um now that sounds like that actually went fairly smoothly yeah right yeah and partially because you did this kind of feasibility test up front right like you knew what you were getting into um but if if you know it's true that internal data science projects don't make it into production that often um uh and and some are going to fail um i've literally heard an executive use the words this can't be another science experiment yeah uh what what would you say to that um the mindset of experimentation i think it's growing you know it was more on the tech companies side uh but it's growing more and more in business yeah and on the data science side i think that if we uh there are different kind of data science projects sometimes that you just need to do some initial modeling and analysis and report what you have you don't need to like productionize it necessarily yeah sometimes that you want to build a model every few years you might use it again you may not productionize it but when you want to productionize it there are a lot more resources that you need around that and sometimes people don't see that part maintenance or turning a model to a product it takes a lot of effort probably more than what they expect and some maintenance of the data products or ai products are usually heavier than software products let's actually dive into that a little bit so let's split that into the maintenance component and then like the the product side yes when you think about taking a data science project and turning it into a functioning product yeah what um what should folks uh on the business side be thinking about in terms of things like resourcing and how the team is structured and in addition to that um and the types of uh um additional budget that would need to go into that as well if i want to do that probably i would prefer to have a very small team at the beginning to run some science experiments you know uh i would not call it science experiments but feasibility experiments yeah and to see that how we can bring in the data different kind of data sources that we need and also the kpis that they want usually that is feasible or not you get a sense of that within a maybe a month or two or sometimes within a week you might get a sense about is it feasible or not when you get to that point uh you can you have much better idea that hey how i can uh build on top of that to turn it to a full-fledged product you know it might take two months it might take eight months or it might take a year to turn that to a product or a business around that um so it sounds like this this initial feasibility experiment that we've been talking about is fairly critical and it's it's worth you know have you had a situation where you ran it and you're like okay this is just not going to be feasible and like how do you communicate that out what is that what does that look like to tell that story to pivot like the ideas happen that we tested and expectation is uh for example the expectation in the accuracy is 90 and if i feel that i can get to 80 like especially if it's in if it's in an area that which i have a lot of experience i probably can say if it's feasible or not sometimes or i would say let's run some initial experiments to see if it happens or not if i my in my initial experiment i don't get i don't reach to 80 probably i would say it's probably impossible to not impossible in the time frame they might need it'd be extremely difficult yeah yeah and uh but uh if i get to 50 i've said forget about that yeah yeah usually that we try to get to 78 or very close to what we want in terms of the final kpis yeah uh to make sure that it's possible that we deliver the next three to six months yeah because especially in the environment that i'm working right now uh it's a very fast pace and we need to deliver within three months or something within six months that yeah we have to deliver a full-fledged product um uh now i know the answer to this so i'm kind of just fishing a little bit yeah but um uh how would you describe to a business person the difference between getting to 80 percent and from getting from 80 percent to 90 and 90 to you know 95 percent yeah like what does that look like and um what types of expectations should a business have around that yeah i would say even before that getting to 80 or 90 preparing the data and having the data ready is a critical thing you know some companies three tech companies they have their data kind of ready yeah you go about you get i just you need to have access to that specific data set you know that can be that can happen within a few seconds right right but in a lot of like traditional businesses the data may not be ready the data might be sitting in different areas uh in silos and or the data has not been collected over time yeah you might need to even build a pipeline yeah you need to build at the mindset of we want to turn this business to a data-driven business you know if that didn't happen yet forget about 80 percent let's build the infrastructure you know got to start there exactly but when you get the data and you want to start what kind of data you need that's also another question if i i'm running a business without having any infrastructure to collect data what can i do there there i would come up with building a very small or simple data collecting pipeline not necessarily again going to turn everything digital or to collect the data no i want to see if there's any value or also what we want to get out of that right now if i have limited budget i will go like that if i don't if i have unlimited budgets probably i will turn everything build that entire infrastructure yeah and i will collect as many data as many signals as possible yeah and later i will put model models on top of that but when you have the data ready usually that you can get access to get accuracy this is what i'm saying it's very very uh uh rough estimate it doesn't happen on every no one's holding it yes you might get to seven eighty percent within like a few days or something a few hours but then you want to get that to higher rates like um 85 90 it might take you a few months when you want to get to the 99.9 it might take you a few years yeah yeah if you like longer time yeah there's exponentially more work right to get just that little bit of extra you know accuracy or confidence out of it and sometimes that you may not necessarily go through just a tradition just one model you might need then train different kind of models for different kind of situations that adds a lot more complexity right right um uh so i like talking about the kind of data infrastructure thing because that transitions into kind of one of the next questions that i have and i want to i want to jump back into that a little bit but um uh i'm seeing a lot of articles uh you know with the title like death of the data scientist um uh is this happening and how do you see uh the role of a data science changing um over time in in response to this um and you know are we going to see you know um uh different technologies that are replacing data scientists is that happening uh if i wanna answer it in short i i don't believe in that yeah data science is probably like software engineering in early 2000s and people are saying oh there are so many people are going into computer science you know and i remember when i wanted to go to school and saying how many did how many software engineers or how many computer engineers they need right right but over time we see is it is the demand for the software engineering is dropping probably not yeah it's growing yeah you know the same for data science but what's happening about data science five years back 10 years back companies didn't know what they want out of a data scientist right they would open a division the use of data science and what do you need i don't know let's put 50 different things on the job requirements and bring people here while they needed a person who was very proficient with excel or with tableau or some other software sure but later they found out okay i need some analysts i need some data scientists who are like specialists in pricing for example or especially in demand forecast who knows natural language processing these are like like companies are growing more mature they know what they want better on the other side a lot of technologies are coming out there uh big cloud service providers like google amazon and microsoft they are providing a lot of apis which helps people to do scrappy things very quickly and that might give you an idea oh doing object detection using compromision is that simple why do i need a contribution expert you know turning that to a product is a very different story right you know and sometimes you may not want to if you have like ten thousand and nothing thousands or millions of pictures that you wanna process you don't wanna do that with those clouds apis right it's probably very expensive you want to you might want to do that on your own with some more sophisticated models that you are going to develop with your custom data yeah that's where that you need the data scientist and also that there are concepts of automl which is coming out and people are talking that hey you can push a button and get results out of that sure there are a lot more to the data science that compared to having just an autonomous system just to push the bottom you need to understand the data you need to prepare the data and feature engineering a lot of those things i have doubt if it's very easy to uh do that with auto and the most important thing is the kpis and business needs and impacts that you want to drive out of the data science problem that you have and that's probably i don't think it's in the near future that's what happened easy to get rid of data scientists and oh i'm pushing about it you know uh uh critical thinking never goes out of style right um and uh no no amount of automl is going to be able to understand um whether or not this is working towards the business's objectives right um so uh i i do get a sense though that you know new data scientists entering the field are kind of just focused on on that aspect of it though yeah um they're focused on the how do i just produce a model um would you agree do you see that what would you tell some of the new data scientists uh entering the field now with the prevalence of these types of technologies uh i would say that what you can bring to the table as a data scientist in addition to ml or all of these technologies out there one thing is that first you have to be practitioner of machine learning you can probably utilize these apis is ready to use tools to to produce that 80 70 accuracy much faster you know that also gives a confidence to the business leaders hey we can if we start bringing a team which can build as a customized model or customer system here in-house that can like exceed it 95 probably you know that's like a lot of like open source tools or autonomous tools or apis available out there they can help you the new data scientist or all probably all the designers can benefit from that when they want to start a new project to just test the idea later you might want to rewrite your own algorithm or use some like combined multiple algorithms to improve that so we just impact a lot on the um uh thinking about the build versus buy implications earlier in the conversation there's an aspect that i really want to get back into which was uh when we first started talking about the depth of the data scientist and infrastructure i think a lot of these two things start to become related because um you know in the comparison that you made with software engineer you start to see much much more specific types of engineers and data scientists you mentioned you know someone who focuses on nlp and businesses we're just kind of blanketing their resumes with 50 things on there what are some of those kind of more specialized verticals that you're seeing and then in addition to that could you maybe speak towards the skill sets that are required on the data management side versus the data science side and if there is a difference between those because it does seem like there's um a fair amount of time obviously involved in managing and cleaning data and companies are hiring the same role to do those parts as well yeah on the skill side that you're definitely seeing more and more specialized people in different areas like nlp and by in nlp are like different like divisions people are experts in natural language generation national understanding external processing and they do like phd's there you know and they are expert there there are a lot of products coming out nlp is probably the hottest area right now yeah and a computer vision probably was two years ago maybe or something like that and computer vision by itself there are plenty of like uh excel fields that people are working on that there are a lot of things are transferable that when you for example you learn deep learning it's it helps you to utilize that use that for um computer vision or nlp for this is like the foundation for that or certain line a little bit like a lstm or uh any of those time streams related deep learning type of model you can use that for time streams demand forecasts it's kind of like problems and that can be like deep learning to me is a foundation you learn that properly and use that and build on top of that expertise in computer vision natural language processing or time series analysis the other side is that you have to like definitely learn the basics in statistical learnings yeah there are a variety of uh very good powerful statistical learning techniques out there yeah and foreign these kind of algorithms are still very powerful very useful and stats is definitely needed and uh your question was around that i'm all right that's correct yeah yeah yeah yeah and then the the uh it was a long question yeah the second part of that is you know um uh the the difference between the kind of data management side and the data science side um uh because i think what i i see sometimes is you know you hire a team of data scientists and then again they're spending you know 80 90 of their time just cleaning and managing data um is there a shift in in roles happening now is it should should the business side when they're writing up um you know job descriptions be thinking about different skill sets for that before to get this question i would like just finish the one thing around yeah previous question uh a lot of times that when you want to develop a skill set uh it's good to find out what kind of industry you're interested to work what line of industry do you love to work on that for the next five ten years you know if you want to do like more of a computer vision related type of things probably will focus on that and learn more if you want to go if or you are in industry right now which needs uh but you need like some specific type of blur some specific type of analysis go and learn and then see what machine how machine learning has been used in that space back to your question about the difference between data management and the data science and is there any shift uh these days in that space uh i would say what i like to hire if we are going to hire people what i would like to see in arizona is like full stack data scientists a person who can like get the data clean it prepare it and and run some models turn that model to an api turn like productionize the model you know right right run a little test and making sure like they have to to me they have to know all of these steps yeah and they have to be comfortable to do that a lot of places you go and see data scientists they don't want to touch the data before it's ready you know i i prefer to have people who are not like that yeah you know you have to like the reason behind it i think that if you don't touch the data if you don't play with the data if you want someone else to take care of your data uh probably you will not have a good understanding about it yeah and if you want to run just one a single model on a data set probably it will be okay but when you are building a product you an industrial in a for industry usually you need to take care of a lot of issues a lot of implementations and you might need to have a very sorry perspective about the whole data which is provided and what kind of model or models you are going to build and to understand the limitations risks and stuff like that you know i think if you play more with data get intimate data it helps you to come up with better models and systems yeah it's a what i'm hearing is that if you're not you know in your own sandbox you know that sand castle's not gonna turn out right you know like uh you've gotta know it in in its deepest format i like to get intimate with the day exactly yeah that's fantastic okay i i you know i was actually thinking um you know because i'm seeing some rules around data engineer come out and i like this concept of full stack data scientists right because we think we've thought about that in you know the computer science world forever you're a full stack engineer oh great you can go to multiple languages you know and i like that concept of you know you've got to have someone who has an understanding of this end to end yeah in order to really be able to get the kind of models and results that that you're looking for exactly especially for a lot of experiments that you run you want to run it like in a very lean way yeah yeah these engineers definitely are needed and if some when you want like a skeleton when you have like different data sources those people they are like probably we underestimate the importance of their roles they do a lot of great jobs to bring the data building a proper pipeline it's not an easy task definitely it's it needs its own professionals sure engineers they come to the picture scaling the data escalating their models and stuff and it needs a lot more expertise just to be a data scientist you got it yeah so uh it's almost it's almost like the data scientist is going to work we're going to run those feasibility tests and they have to do that early massaging of the data right to be able to get to that feasibility level understand if they want to move on from there truly run the types of experiments to understand it and then when you're ready to scale that model up um that's when you might bring in a data engineer to help make sure that okay exactly that makes perfect sense yeah um uh my last couple of questions for you um and this has been fantastic i uh you know and we've touched on a lot of this already but um one of the things that i'm seeing the most of you know traveling around social media on linkedin is that venn diagram with you know stats computer science and the business category um and you know in the middle of that venn diagram is data scientist and we've obviously been focused a lot on on the business side of things and i want to zoom in if we're picturing that venn diagram i want to zoom in on that circle what are the types of skills that you think are are most critical for the data scientist um within if we're zooming in on that business circle because you know i understand computer science and stats right they're they're fairly well um uh self-contained but business is just like it's a broad thing to have there yeah um so what are some of those skills we talked about obviously identifying the kpi isn't working but what would you uh say are the most important ones like understanding the communication is probably uh it will have everything in it but still it's probably very broad to say communication it can contain that understanding the kpis understanding the impacts of that understanding the dollar amount assigned to that the performance that you are going to have and or the mistakes that you might make these are the things that generally it's important for data it should be important for data scientists prior to going uh to build the marlin system and as i said and also understanding the time limits that if you have one week to deliver or you have a year to deliver you might do very different things you know and again probably people who are a lot of data science they have like phd background in some different quantitative fields they might come oh from the research mindset hey i'm going to solve this problem a year or two no five years no this is in business it's important that hey we are going to time box that we want to see the impact in a limited time yeah are we going to deliver that or not yeah that driving impact might happen just through running some simple regression you know right you don't need necessarily to run heavy deep learning problems you know sure and under technology for the sake of technology yeah and then there are a lot a lot more aspects what is the computational budget that we have what is the speed that we need these are around the problem that we have to communicate that prior to getting to solving the problem again we touched a little bit on this but how would you recommend um a data scientist team be built out if you're going towards a if you're if you're a business and you have a data science problem that you're trying to solve that you're trying to actually turn into a product um what are the key roles that the data scientists should be interacting with like who are the other people outside of the data science organization you know since communication is so important that they should be having regular touch points yeah in terms on the business side it's it's important that uh the data science manager or the lead to have a frequent touch base with the business leaders the ceo general managers you know to understand what are the business needs you know and why we are really doing that what's the expectation managing the expectation you know that's probably a very critical role to do as a data science leader managing expectations in any situation can be very tough uh yeah i think that data scientists have an extra layer of difficulty right exactly yeah and also in data science a lot of data science problems you are in an experimentation mode yeah it might have worked for other use cases but when you are kind for this specific use case you need to test it may not work properly you know for any reason there might be a variety of reasons this is important to be like this expectation needed to be managed you know and also time wise it's not like a lot of software problems have been solved before yeah and you say oh somebody built that api build that app with that website you know these are like repeatable things it should be like fairly easy to estimate yeah for the time for the budget for the team but in data science it may not be as easy to estimate as uh software products are right right and other than that for the data science people in the team it's important to communicate constantly with product managers and to see what are the kps needed where they are you know right any changes it's needed or any issues that they have to make sure that products will be delivered on time and project delivered on time at the same time they are working on the right thing you know yeah we tend to work on what we like to work on yeah and it might deteriorate you from the important stuff yeah there's lots of cool things out there to work on you know they're not always the things that need uh the most attention you know as a product manager i feel that yeah my last question for you um uh what do you think is one of the most important things that you would tell a data scientist coming up in the field today and again i think we've touched on this a little bit but if you had to narrow it down to one kind of thing what what would you want them to know it will be probably depending on the field industry they are going but definitely it's important to know the foundation of the data science but in a business that you are going it's important to get to know the business very quickly like one of the suggestions when i was finishing after my phd before i started my first job after phd one of my friend told me that go and learn acronyms of that company that technology because the jargon i think how many acronyms they have and i mean they're found oh there are if not more than a thousand acronyms were there yeah people were using like different groups it seems that everybody knew that yeah yeah like it took a while for me to learn absolutely and that helps you to understand the data the business better you know and the other thing probably i would say question whatever you're working on make sure you understand what are the impacts and what are the your time frame like all the aspects of the problem as much as possible question that not in a negative way or out of curiosity that curiosity is very important love that yeah thank you likewise it's a pleasure thank you cheers thanks

Channel: GravityAI

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Id: QZS7R0cJIGs

Length: 41min 10sec (2470 seconds)

Published: Sat Jan 16 2021