Staff Data Scientist-28283 (Bengaluru)

ANSR is hiring for one of its clients. About ANSR MedTech: Who We Are: ANSR MedTech Capability Center is a new global innovation hub being established in India for a Fortune 100 Fastest-Growing Company in the MedTech sector. Built in partnership with ANSR, the center draws on ANSRs proven experience in establishing and scaling high-performance Global Capability Centers (GCCs) for leading global enterprises. ANSR MedTech center brings together world-class engineering, product, and technology talent to build next-generation healthcare platforms and solutions that power global operations. Our Vision: - To build a next-generation MedTech capability center that powers global healthcare innovation. We envision: - High-impact innovation hubs shaping global product and technology roadmaps. - Centers that go beyond support functions to drive core engineering and platform development. - Sustainable, scalable ecosystems that nurture world-class MedTech talent - Capability centers that directly influence patient outcomes worldwide At its core, the ANSR MedTech Capability Center is about enabling innovation that touches lives at scale Job Title: Staff Data Scientist Location: Bengaluru, India About the Role: The Senior Data Scientist will be a core contributor within the India COE data science practice, responsible for independently designing and executing statistical analyses, developing predictive models, and translating complex data into transparent, defensible insights that inform business decision-making. This role partners closely with the business partners to convert business questions into well-scoped analytical problems and works with Data Engineering, Analytics Engineering, and AI Engineering to ensure outputs are reproducible, governed, and actionable. The Senior Data Scientist is expected to execute end-to-end analytical work with limited oversight while contributing to shared methods, reusable code, and model documentation standards. Scope of Responsibility: Statistical Analysis & Quantitative Methods: - Design and execute statistical analyses in response to business questions including hypothesis testing, significance testing, power analysis, and confidence interval estimation - Apply causal inference techniques to observational data where controlled experiments are not feasible including difference-in-differences, regression discontinuity, propensity score matching, and synthetic control - Develop and execute A/B and multivariate testing frameworks: randomization design, sample size calculation, holdout group construction, and interpretation of results with appropriate uncertainty quantification - Conduct time-series analysis including decomposition, forecasting, and anomaly detection across business and operational metrics - Apply survival analysis and event-based modeling to understand time-to-event patterns and duration dependencies in the data - Build segmentation and clustering solutions that identify meaningful structure in complex datasets and support strategic targeting and prioritization - Document all analytical work with clarity: methodology, assumptions, limitations, sensitivity analyses, and confidence in conclusions suitable for peer review Predictive Modeling & Machine Learning: - Develop, validate, and monitor predictive models including propensity scoring, churn modeling, demand forecasting, and anomaly detection grounded in statistical best practices - Apply feature engineering, model selection, regularization, and cross-validation techniques to build models that generalize reliably beyond training data - Evaluate model performance using appropriate metrics for the problem type balancing accuracy, interpretability, calibration, and operational feasibility - Produce model documentation covering methodology, assumptions, validation approach, performance benchmarks, and known failure modes - Monitor deployed models for drift and degradation, and participate in retraining and re-validation cycles as needed - Collaborate with AI Engineering when models are ready for production deployment providing methodology documentation and supporting the handoff process Data Exploration & Problem Framing: - Partner with business function to translate business questions into well-scoped analytical problems with clear success criteria and measurable outputs - Conduct exploratory data analysis (EDA) to understand data distributions, quality issues, and structural patterns before committing to an analytical approach - Advise on measurement strategy, testability, and data requirements upstream flagging when a question cannot be answered reliably with available data - Identify the appropriate analytical method for each problem knowing when simple statistical tests are sufficient versus when more complex modeling is warranted - Surface data quality issues discovered during analysis and escalate them to Data Engineering with clear documentation of the impact Analytical .