Ford Motor Company Lead Data Scientist
University of Louisville Bachelor’s Degree, Business, Computer Information Systems
Current Time 0:00
/
Duration Time -:-
Progress: NaN%

How did you get to where you are today? What is your story? What incidents and experiences shaped your career path?

Summarized By: Jyotsana Gupta on Fri Mar 06 2020
I would say that my story is long and interesting one not sure if we have all the time needed for it, but I would say that most of the relevant bits begin when I started in my undergraduate college career. I come from a background where you're lucky to be able to afford to go to college, so I was fortunate enough to get the scholarships and things I needed to be able to attend. What kind of got me to where I am today is what I would say is kind of a combination of my education at the University of Louisville in addition to other resources, so they really boil down to the three things and those three things would be dedication, hard work, and leverage. I think it's important to leverage anything you have at your disposal in order to get where you need to be and to be dedicated to your own success. So that played into my story and kind of how I dealt with certain incidents that shaped my career, such as developing relationships with mentors, leveraging online learning platforms like Coursera, edX, Datacamp, and Udacity basically, you name it to get the knowledge and the skills that I needed. All of that has been a combined effort, it has been one supplemental to the other in order to get me to where I am today.

What are the responsibilities and decisions that you handle at work? Discuss weekly hours you spend in the office, for work travel, and working from home.

Based on experience at: Lead Data Scientist, Ford Motor Company
Summarized By: Jyotsana Gupta on Fri Mar 06 2020
So responsibilities that I handle at work, I'm the lead Data Scientist for FordPass and Lincoln Way globally for our analytics team here and responsibilities that I handle are managing the team, prioritizing tasks, working on more in-depth analytical projects as the lead data scientist, I would be the one with the most experience and the widest skillset. The decisions that I handle at work are for prioritizing tasks and kind of interfacing with the business to get the business what they need in order to perform at their best. Sometimes that can be challenging and it can involve a lot of meetings it can involve a lot of discussions with the team and with the business to make sure that we do provide those insights and those analytics that are needed. I would say that right now, I work a little over 40 hours a week and that split between home, the office and abroad. My most recent trip was actually to Chennai, India for two weeks interface with our team there.

What tools (software programs, frameworks, models, algorithms, languages) do you use at work? Do you prefer certain tools more than the others? Why?

Based on experience at: Lead Data Scientist, Ford Motor Company
Summarized By: Jyotsana Gupta on Fri Mar 06 2020
We use a pretty diverse set of tools, I think, ranging from programming languages to tools that are more GUI based. So there's a lot of R and a lot of Python here, which makes sense and those are the two primary open-source data science languages we have a lot of that. We also have a strong presence of tools like ClickView and Alteryx. Other tools, like DataRobot and some more tools that are specific to certain parts of the business, like Rally for project management or other things like that. As far as algorithms are concerned though we have, I think if you took a catalog of the algorithms that we use, I think at least every single one that exists would be used at least once for something. We have the familiar favorites, like some classification algorithms like, of course, XGBoost is really popular right now that we do use a lot, but I would say some of my favorites, I mean, I don't know if I have favorites, but I do prefer to do a lot of heavier duty analytics and more in-depth visualization with R and Python, as opposed to some of those other tools just because I have more freedom to do what I know needs to be done and I have more ability to leverage my skills, the GUI abstracts so much from the user that if you need to do something very specific, it may just not exist but with a programming language, you can basically do whatever is needed.

What things do you like about your job? Were there any pleasant surprises?

Based on experience at: Lead Data Scientist, Ford Motor Company
Summarized By: Jyotsana Gupta on Fri Mar 06 2020
So there's a lot of like about my job here. One is that I have the freedom to, I would say do what needs to be done to do what's best for Ford, and that involves a great many things that could be working with my own particular style or working as I see best. I have that trusted relationship with the business such that if I need to work for home because I need to concentrate, there is no formal process to say, I'm working from home and these are the reasons, I can just do what needs to be done. Some pleasant surprises, I would say, would be when I first got here, I wasn't really sure how an older company would handle my energy, my mind, and my general behavior. But it's been really well received. I like to have fun in the office but also focus on getting things done. It's also cool to work with a global company that has a ton of different tools available. There are a great many things that are on the bleeding edge that we have for some autonomous vehicle projects we have going on. There are a bunch of different tools that we have at our disposal, and those come along with the large budget that a global company has to facilitate the purchase of those tools.

What are the job titles of people you routinely work with inside and outside of your organization? What approaches do you find to be effective in working with them?

Based on experience at: Lead Data Scientist, Ford Motor Company
Summarized By: Jyotsana Gupta on Fri Mar 06 2020
Most of the people I work with are data scientists or data analysts at some level we are. I interact with a lot of businesspeople that range from could be an entry-level manager all the way up to directors and senior executives and then the approach is very tailored. So a data scientist or an analyst will want a lot of details about specific projects, tasks required, you name it but somebody, as you go higher up, you have to tailor your approach more and more to be more concise and direct about exactly what you think the person you're speaking with cares about and what they need to know. You have to be very strategic with your time and even your word choice sometimes to make sure that they understand exactly what you're saying and that you answer their questions very directly. A data scientist or somebody that's more operational will have more time for details for discussion. Somebody that's director level and above will not, so again that the conversation has to be very not practiced robotic but more focused and strategic as what I found.

What major challenges do you face in your job and how do you handle them? Can you discuss a few accomplishments?

Based on experience at: Lead Data Scientist, Ford Motor Company
Summarized By: Jyotsana Gupta on Fri Mar 06 2020
Some major challenges that I face are with regard to data legacy IT environments. If you go to newer companies, especially tech companies, you will find that analytics is pervasive throughout the company itself and the architecture with them. That is not the case with older companies, a lot of older companies and especially in the automotive industry, like where I at, I am still pretty young, but I'm not that young and I end up running into IT systems that are older than I am, databases that existed before I was even born and that can present a challenge because Stack Overflow and Google are not necessarily as helpful with a 35-40-year-old database system that is still critical to business function. Some other challenges could be working with a very diverse group of people. They may or may not have the same technical breath even the same business breath and maybe from completely different functional areas. You could be talking to somebody who manufactures a car, to somebody who is completely financially responsible for marketing campaigns and targeted incentives for vehicle purchases, those are two very distinct skill sets. What I usually do to handle them is, I bring together either some common understanding, some common knowledge between all the different groups and myself to kind of glue that group together and then begin to discuss pointed things and very focused things about each of their individual responsibilities so that we all feel like we're on the same page and then everybody has some facet of that conversation that anchor them into what we're talking about. A couple of accomplishments, I think I can talk about are, I led a cross-functional team that was very much as I just described to create a very large and very important data product here and it's not only been a great help for some of our core competencies, it's also now a source of revenue for the company itself. There have also been some more analytical types of projects that have supported some older parts of the business in a very substantial way and it's the same thing again, where people from very different parts of the business, parts of the analytics organization, part of IT that have to come together for a common understanding and a common goal of which in these particular cases was led by me snd that strategy for doing so was, as I described earlier, anchor in points of the conversation, individual team competencies, and then glue that all together with an organizational goal or the analytics itself.

What are the recent developments in the field? How significant are these improvements over past work? What are their implications for future research & industry applications, if any?

Based on experience at: Lead Data Scientist, Ford Motor Company
Summarized By: Jyotsana Gupta on Fri Mar 06 2020
Some recent developments in the field, especially with regards to apps, analytics, targeted marketing, and user experience and user interaction have been that the ability to focus on an individual user has zoomed in a great many times over the past, I want to say over the past, about five years, it used to be for applications whether they are Web apps or apps on the phone. A targeted user group would be an archetype or a persona that identifies a subset of the population that we would then focus on specifically. Now, however, we can get down to much smaller populations of people and even individuals to see what was their journey, what was it like? What are the things that they like, what are the things that they hate? We use all of the tools that I mentioned previously in order to get to this goal but some of these improvements have very significantly impacted how we speak to customers, how we interact with them through our application as an extension of our business to get engagement with them and to make it part of their experience with our company, not necessarily just the product itself, and I think that will continue in the future. I think we will see more and more individual focus with the application where it feels like each product that you have and the application that's associated with it kind of go hand in hand for you as a person and instead of it just being, "This is my car and drives me to work" it's more like, "I have experience with this brand" and it's based on the product, the car in our case, the app and then the individual person. I think that's going to continue more and more into the future for almost any industry that can.

What qualities does your team look for while hiring? What kind of questions does your team typically ask from candidates?

Based on experience at: Lead Data Scientist, Ford Motor Company
Summarized By: Jyotsana Gupta on Fri Mar 06 2020
There is a saying that we have here when it comes to candidates' specific things it's, a lot of people focus, especially in the resumes and in interviews on I know are R and I know python, I'm the best ever at machine learning or even classifications, or I have a Ph.D. in optimization but what we really look for is a combination of a few things, and this is the saying that we've had for a while, we generally focus on smart, nice and curious. I think it's more important to have kind of a curious mindset to always be learning because, especially in the analytics field, things change very rapidly, and you need to be curious about different parts of the business because you learn the business through the data and if you don't know the business or the data, you can't do data science, you can predict something, but it may or may not even be valuable or the target the business wants you to focus on. You need to be nice to be able to work with the business to work with your co-workers. If people hate working with you, you're probably not going to be a good fit in many different organizations and if you're not smart, it's also going to be a challenge. I think one thing that we can say that we focus on for candidates and what specific things we ask them would be to challenge them to think about solving a problem that could be vague and then analyzing how they would approach that problem, it's usually something strange, something they've never heard of before and ability to kind of gauge how they would think through a new problem. We also ask them about some more pointed questions for instead of, I developed a dashboard to help finance, for example, what was the business value of that? So you developed a dashboard for finance, what did it do exactly? Did it save money? Did increase money? Did it help make things more efficient? I would say focus on that much more than the technology than the actual product itself because everybody needs dashboards but how exactly did you make it? How did you make the organization more fit and more effective using your tool?.

What is a typical hiring process for a job like yours? What are the titles of people who interview? What questions usually get asked and how to handle them?

Based on experience at: Lead Data Scientist, Ford Motor Company
Summarized By: Jyotsana Gupta on Fri Mar 06 2020
The typical hiring process for people like myself, data engineers and scientists is relatively consistent with the questions I posed earlier, checking for kind of how the person thinks and how they approach problems, checking for how in their past experience, they focused on business value and leveraged analytics to that end? How they work with data, how they approach data that they have never seen before? And especially if they have experience with real-world data because real-world data is nothing like what's in the tutorials. Even the tutorials that have data cleanup, actual real data looks nothing like that, it's much noisier, things will be encoded or cryptic. We talk a lot about these things, and in the interview process, you usually have managers and some faculty, you will have sometimes technical experts that could be a developer or could be a data scientist, but they're usually at manager level and above. We have different approaches, sometimes it's the questions about thinking how you framework new problems, sometimes it is about more specific things, like analyzing a data set on the fly in the interview, scaffolding out in architecture for a new data model or a new data product or it could be even sketching out a dashboard, something like that, and then saying from this, you've developed something, how do you think it impacts the business and what value do you think it drives? It's usually pretty varied, but that's generally the core of the interview process.

What are different entry-level jobs and subsequent job pathways that can lead students to a position such as yours?

Based on experience at: Lead Data Scientist, Ford Motor Company
Summarized By: Jyotsana Gupta on Fri Mar 06 2020
We have a lot of data scientists who are what I would define as entry-level, some of them are undergrad, but most of them are out of graduate programs that are somehow involved with analytics. However, we do bring in people with business degrees, sometimes people with even art degrees or policy degrees. They either start out as a junior entry-level analyst or an entry-level data scientist, depending on their experience. A job pathway that leads to where I'm at sometimes varies. Some companies are more rigid about it, some companies or not. I would generally say that more often than not, it is a graduate program or an undergraduate program that has a focus on Data Analytics or some type of information systems type of degree. Anything like that can get you a more direct path to like where I started here, but really it could depend on your experience and your drive. If you have a drive or if you have a curiosity for analytics, you can generally find some way to get into a company at an entry-level analyst or data scientist position.

How did the school prepare you for your career? Think about faculty, resources, alumni, exposure & networking. What were the best parts?

Based on experience at: Bachelor’s Degree, Business, Computer Information Systems, University of Louisville
Summarized By: Jyotsana Gupta on Fri Mar 06 2020
One way that my college prepared me for this is just kind of the overall program. So I was a computer information systems major at the University of Louisville, and there was a lot of application development and Web development that was involved, which kind of piqued my interest in the development in general in programming and then there was one particular thing that I think was really good, we had an elective that was called data mining, and it was not a popular elective. There were maybe 10 people that signed up for my class when I was in and I think as soon as some calculus was shown, I want to say five of us remained but that class, in particular, got me hooked. It was that class that really got me hooked into analytics. Just a very brief introduction to very simple things, regression, gradient descent, very brief simple introduction to neural networks that's kind of where it started. There were some other aspects, such as not necessarily like career resources we had formally but just mentoring relationships with the faculty, with people in the community from different parts of the school. I had mentors in the CIS program, but I also had mentors in the business school abroad, especially the entrepreneurship program those two put together were probably the best because you have the technical networking experience expertise and then you have the business expertise and networking. I think all those puts together were a very good start to how I got where I am.

How did the school prepare you for your career? Think about faculty, resources, alumni, exposure & networking. What were the best parts?

Based on experience at: Associate's degree, Electronics Engineering, Owensboro Community and Technical College
Summarized By: Jyotsana Gupta on Fri Mar 06 2020
I would say that for the two associates degrees, those were much more technical. I would say that those helped me develop more of an analytical mindset, especially when it comes to troubleshooting electrical systems or fluid power systems. It also helps to have that knowledge when you go into an industry that's more in tune with manufacturing because I've had several projects where part of that involved getting into the plant and analyzing a process or understanding how my products affect the manufacturing process. I think that was more of the tie end, there wasn't as much networking or alumni, resources at the community college. I would say it was more the curriculum itself that helped and I mean, it was all great, getting my hands dirty with fluid power systems and hydraulics, I have been able to get into the lab and create like simple electrical things like robotics and understand how that works in the manufacturing setting, I would say that was the best.

Would you like to share something that is not on your resume? This may include your passions, facing setbacks or adversities, a unique experience, or an unexpected help.

Summarized By: Jyotsana Gupta on Fri Mar 06 2020
I would say something I do not put on my resume but has definitely been both an adversity and something that's been a challenge for my life is, I was actually enlisted in the Air Force for a time. I did my best, I was not as successful at it as I wanted to be, but it was definitely a rewarding experience, it was definitely eye-opening and it definitely helped me achieve the level of self-discipline and dedication that was needed to really launch me to where I am right now. I think those traits are important and as I mentioned in the interview, it's not necessarily about how good you are with Python, it's kind of that mindset and who you are as a person to do what needs to be done, that self-discipline and dedication towards your own success, I think are very important.

Do you have any parting advice for students hoping to get to a position such as yours? What 3 dos and 3 don'ts would you suggest?

Based on experience at: Lead Data Scientist, Ford Motor Company
Summarized By: Jyotsana Gupta on Fri Mar 06 2020
My parting advice for current students to get to where I am is, focus on real-world application, doing machine learning tutorials for handwritten digit recognition and the titanic data is always interesting and fun but anything at all that has a more tangible benefit to any kind of business would be something I would focus on now if students have a particular industry or a particular company they wish to focus on, I recommend a very targeted and strategic approach that focuses on value, not something like the model's accuracy or the models structure itself, I would absolutely focus on those things first. The three do's and don'ts for resume specifically, I would absolutely include any kind of volunteer experience, community experience, anything like that. I would absolutely focus on anything that has a tie in with business value, anything that has anything that presents value to the business itself. I would focus on keeping it brief, which includes paring down your experience, maybe even creating a table for your technical aptitude and I've seen this with a lot of resumes, especially from PhDs. Three don'ts, keep the bullet points to a minimum. I actually went through a resume one time that was 10 pages of bullet points, please don't do that. Another is you don't have to embellish too much on things you've actually done. Keep it simple, focused and realistic. I've gone through several resumes myself, where I will look at some experience and I put down and say, "There's no way you did that" and that immediately kicks out your resume, focused, simple and real. Another thing I would say don't do is don't focus too much on technical details, don't focus too much on maybe the project itself. Keep things broad and keep things generic enough to be applicable for many different projects, many different businesses, and teams, because again it's culture fit, it's your ability to learn and grow and who you are as a person that I think helps define your resume then, once I'm interested in that, I will continue into the details.