Data Science Intern

The Data Science Intern will contribute to real-world projects at the intersection of business, economics, and data science. As part of the Enterprise Data Management team, the intern will support efforts to develop machine learning models, explore Generative AI (GenAI) and large language model (LLM) use cases, and analyze financial and operational data to help inform business decisions. Working alongside our Machine Learning Ops (+GenAI) Engineer, Platform Engineering team, and Data Stewards, the intern will gain hands-on experience with a modern Databricks Lakehouse environment and learn how analytics and AI solutions are developed, tested, and deployed in an enterprise setting. This internship offers a unique opportunity to stretch beyond the classroom, applying technical skills to real-world challenges, while learning from experienced professionals in a leading investment management firm.

• Contribute to the end-to-end analysis of real datasets—framing the question, applying statistical, econometric, and economic reasoning, and surfacing insights that help inform investment and operational decisions.
• Translate quantitative findings into compelling narratives, visualizations, and recommendations to help influence how the team and business stakeholders take action.
• Support the development and iteration of statistical, econometric, and machine learning models across the full workflow—from feature engineering to validation—with mentorship from senior engineers and business SMEs.
• Prototype GenAI and large language model (LLM) applications—such as retrieval-augmented and agentic workflows—that make firm data easier to explore, analyze, and act on.
• Partner with our MLOps (GenAI) Engineer to move promising prototypes toward production, learning how models are deployed, monitored, evaluated, and scaled.
• Research emerging techniques in machine learning, generative AI, and applied econometrics; run structured experiments and causal analyses; and present findings and recommendations to the team.
• Work with the Platform Engineering team to build, query, and analyze data on our modern Lakehouse platform, gaining hands-on exposure to production data infrastructure.
• Partner with Data Stewards to understand data lineage, definitions, and quality, and apply responsible, well-governed data practices in your work.
• Contribute to data quality and model evaluation—building test cases, scoring model and LLM outputs, and helping ensure analyses rest on reliable data.
• Learn and apply best practices for data privacy, security, and responsible AI, operating within the firm's compliance and governance framework.
• Collaborate across Data Science, MLOps (GenAI) Engineering, Platform Engineering, Data Stewardship, and business teams to help scope ambiguous problems and deliver measurable results.

Required

• Currently pursuing a bachelor’s or master’s degree in Economics, Data Science, Mathematics, Statistics, Finance, or a related quantitative field—including combined programs such as Economics & Data Science.
• Strong academic (and/or extracurricular or relevant internship) foundation in mathematics, statistics, and econometrics, such as probability, linear algebra, regression, causal inference, and hypothesis testing.
• Programming ability in Python (or R), with familiarity with data libraries such as Pandas, NumPy, or scikit-learn.
• Demonstrated quantitative ability through coursework, research, hackathons, competitions (e.g., Kaggle), or personal projects in data, analytics, or AI.
• Exposure to applied machine learning or data mining through coursework or projects, and genuine interest in generative AI, with a drive to learn how modern models are built, evaluated, and applied to real business and investment problems.

Preferred

• Prior internships or hands-on projects involving machine learning or GenAI.
• Familiarity with data management concepts and SQL (or a strong aptitude and eagerness to learn them quickly).

• Strong communication skills, with the ability to bridge technical teams and business stakeholders and explain complex ideas clearly, in both written and verbal form.
• A collaborative mindset and eagerness to work with people across different teams.
• Exceptional analytical and problem-solving skills, with intellectual curiosity, high standards, and a proactive, self-directed approach to ambiguous problems.
• A passion for data as a tool to inform and drive real-world, business value.