Data Scientist Associate

Round 1: Online assessment (SQL & Python coding) → Round 2: Technical interview (statistics, modeling, case studies) → Round 3: HR interview (fit, motivation, cultural alignment)

JPMorgan Chase promotes a performance‑driven yet inclusive culture where employees are encouraged to innovate and take ownership. The firm offers structured learning programs, mentorship and a balanced work‑life environment, allowing analysts to grow both technically and professionally.

The interview process usually starts with a timed online assessment focusing on SQL queries and Python coding. Successful candidates move to a technical interview where they discuss statistical concepts, predictive modeling case studies and may be asked to solve a live problem. The final HR round assesses cultural fit, communication skills and motivation. Preparation should focus on data manipulation, hypothesis testing and clear articulation of analytical thinking.

B.Tech/B.E., M.Tech/M.E., B.Sc. or M.Sc. in Computer Science, Statistics, Mathematics, Engineering or related fields; minimum 60% aggregate (or CGPA 6.0/10); no active backlogs; graduating batch 2024‑2026; strong analytical mindset and willingness to work in a collaborative environment.

JPMorgan Chase & Co. is one of the world’s oldest and most respected financial institutions, with a legacy spanning over two centuries. The firm operates across investment banking, consumer banking, commercial banking, asset management and financial transaction processing, serving millions of customers and thousands of corporations worldwide. In India, JPMorgan Chase has built a strong presence in technology, analytics and corporate functions, offering a collaborative environment where innovation meets rigorous financial discipline. The company prides itself on a culture of inclusion, continuous learning and a commitment to delivering superior client experiences. The Data Scientist Associate role sits within the International Consumer Bank’s Business Analytics team in Bengaluru. As a member of this high‑impact squad, you will partner with product managers, marketers, engineers and operations leaders to turn raw data into actionable insights. Your day‑to‑day work will involve designing lightweight predictive models, running controlled experiments, and translating statistical findings into clear business recommendations that drive customer retention, cross‑sell opportunities and overall profitability. Key responsibilities include: 1. Deliver high‑quality analyses and predictive models while participating in peer reviews to raise analytical standards. 2. Work closely with cross‑functional stakeholders to frame business questions, define success metrics and produce decision‑ready recommendations. 3. Support measurement of key customer journeys, focusing on engagement, retention and unit economics. 4. Design, execute and evaluate A/B and other controlled experiments, handling test plans, guardrails and sample‑size calculations. 5. Apply statistical techniques to quantify impact, segment customers and communicate trade‑offs. 6. Build, validate and maintain interpretable predictive models such as churn risk, response propensity and customer value proxies. 7. Follow best practices for reproducible analysis, documentation and code review. 8. Collaborate with data engineering, analytics engineering and dashboard developers to ensure data readiness and operationalisation of insights. 9. Communicate findings through compelling narratives for both technical and non‑technical audiences. 10. Influence product road‑maps and business decisions with evidence‑based insights. Tech stack typically includes Advanced SQL, Python (pandas, scikit‑learn, statsmodels), Jupyter notebooks, Git for version control and basic data‑visualisation tools like Tableau or PowerBI. The role offers a clear growth path from associate to senior data scientist, with opportunities to lead squads, specialize in causal inference or move into product analytics leadership. Joining JPMorgan Chase means working in a globally recognised brand that values diversity, offers robust mentorship programmes, and provides exposure to large‑scale financial data, making it an ideal launchpad for ambitious analytics talent.