Data Science and AI Career Pathways Panel Recap

Panelist Perspectives:

Anahita pursued a PhD in Physics with a plan to be in academia, but she discovered consulting and was attracted to the concept of problem solving and working with multiple companies. As an immigrant, she also had a desire to understand business in the US, so she worked at BCG for two years post-PhD. She came to Instacart because of her alignment with the company’s mission focused on access. Describing Instacart’s four-sided market place, she feels there are lots of interesting data problems including optimization problems. She notes there are also challenges in staying relevant (i.e. on top of AI trends) and modifying the delivery algorithms to account for factors like geography, weather and time of day.

Connor received his Masters in May ’22 and worked in a biotech organization that focused on applying data to treat blood infections more quickly. He felt the work was more data analytics than machine learning and he wanted more of the latter so he transitioned to his current role at OnCorps. Now he works at a 50-person startup where his tasks sometimes include project management as well as engineering. He found continual learning outside of work to be very important to his career growth. He engaged with Coursera courses, and has continued to TA for data courses at Harvard and this has helped him maintain his technical skill and stay on top of trends too.

Ria received her undergrad degree from Harvard Extension School and continued to get her Masters in Data Science. She has since worked in AI at Intel. She noted that there is definitely a lot of hype about generative AI. That being said, she thinks the fundamentals (Python, R, C++, and Big Data Processing) are still important to build in order to succeed in the data fields. She also emphasized that the soft skills of data storytelling are essential.

Melvin was a Government concentrator at the college with a secondary in computer science. He now works at Meta in what he describes as a ‘product data science’ role, which focuses less on engineering and more on thinking about how data will be used. He recommends students who are considering entering the data fields, think about what problems that exist out there and what they want to try to solve. The trends he is observing include the increased use of modeling, especially for optimization and understanding the underlying causal mechanisms. For example, it is not just about discovering that someone is a high revenue user, but what makes them one.

Resources for Further Exploration:

• Kaggle– AI and ML community, has data sets and competitions
• KDNuggets– a leading site on Data Science, Machine Learning, AI and Analytics
• Data Science Courses on Coursera
• Explore different careers in AI
• Artificial Intelligence Careers: How to Break into the AI Field According to Experts
• Prepare for data science interviews with Ace the Data Science Interview

Leverage MCS Networking Resources:

• MCS Firsthand Advisors  
• Harvard Alumni Association Directory  
• Making Connections Guide

Final Words of Advice:

• Anahita: “Network! Find a mentor.“
• Connor: “Be a TA, learn the tech more deeply–Stats 101, foundational skills–are so important. Understand the model and why you are using it-especially for interviews.”
• Ria: ” Join an open source community—have an open mind. Mental health—recognizing the importance of your work in the bubble that it is in. Recognize your expertise and know your value.”
• Melvin: “Even in the best companies there are different problems to solve and some don’t excite you. Communicate with stakeholders what you are looking for—ask ‘what are the problems that I should be solving on this team?’ Recruiters are human beings and a good source of information; get to know them. Keep connections alive.”