Data Scientist with SQL, Python, and Fintech

Duties:

Performs business analysis using various techniques, e.g. statistical analysis, explanatory and predictive modeling, data mining. Determines best practices and develops actionable insights and recommendations for the current business operations. Works directly with the internal or external client to identify analytical requirements. May help to produce ad hoc data and reports. May assist in implementing or developing systems to capture business operation information. May occasionally guide less experienced business data analysts.

Skills:

• Advanced SQL skills and proficiency in visualization tools such as Qlik, Quicksight, Tableau, Plotly Dash.
• Strong analytical and modeling skills using Python (for its rich suite of statistical and modeling libraries like NumPy, Pandas, Scikit-learn, etc.).
• Experience with Causal inference techniques such as propensity score matching, difference-in-differences, and synthetic control methods.
• Experience with Generative AI and other evolving technologies to accelerate insights from multi-modal data.

Education:

• BS or MS degree in Statistics, Mathematics, Computer Science, or a related field.
• 5+ years of experience in data science or product analytics (preferably in fintech), with a strong foundation in predictive modeling, customer segmentation, and experimentation.
• Ability to formulate data-backed strategies that will drive step-function growth for the business and increase customer benefit.
• Ability to generate hypotheses grounded in customer behavior, industry trends, and external market factors.
• Experience in designing and interpreting complex experiments beyond traditional A/B testing methods.
• Demonstrated experience in building reusable and scalable analytics solutions, with a focus on efficiency and avoiding duplication of work.
• Outstanding communication skills with the ability to influence decision-makers and build consensus with teams.
• Quick learner, adaptable, with the ability to work independently or as part of a team in a fast-paced environment.