Staff Data Scientist [T500-28283]

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 ANSR’s 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 scaleJob Title: Staff Data ScientistLocation: Bengaluru, IndiaAbout 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 clear, 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 estimationApply causal inference techniques to observational data where controlled experiments are not feasible — including difference-in-differences, regression discontinuity, propensity score matching, and synthetic controlDevelop and execute A/B and multivariate testing frameworks: randomization design, sample size calculation, holdout group construction, and interpretation of results with appropriate uncertainty quantificationConduct time-series analysis including decomposition, forecasting, and anomaly detection across business and operational metricsApply survival analysis and event-based modeling to understand time-to-event patterns and duration dependencies in the dataBuild segmentation and clustering solutions that identify meaningful structure in complex datasets and support strategic targeting and prioritizationDocument all analytical work with clarity: methodology, assumptions, limitations, sensitivity analyses, and confidence in conclusions — suitable for peer reviewPredictive Modeling & Machine Learning:Develop, validate, and monitor predictive models — including propensity scoring, churn modeling, demand forecasting, and anomaly detection — grounded in statistical best practicesApply feature engineering, model selection, regularization, and cross-validation techniques to build models that generalize reliably beyond training dataEvaluate model performance using appropriate metrics for the problem type — balancing accuracy, interpretability, calibration, and operational feasibilityProduce model documentation covering methodology, assumptions, validation approach, performance benchmarks, and known failure modesMonitor deployed models for drift and degradation, and participate in retraining and re-validation cycles as neededCollaborate with AI Engineering when models are ready for production deployment — providing methodology documentation and supporting the handoff processData Exploration & Problem Framing:Partner with business function to translate business questions into well-scoped analytical problems with clear success criteria and measurable outputsConduct exploratory data analysis (EDA) to understand data distributions, quality issues, and structural patterns before committing to an analytical approachAdvise on measurement strategy, testability, and data requirements upstream — flagging when a question cannot be answered reliably with available dataIdentify the appropriate analytical method for each problem — knowing when simple statistical tests are sufficient versus when more complex modeling is warrantedSurface data quality issues discovered during analysis and escalate them to Data Engineering with clear documentation of the impactAnalytical Quality & Reproducibility:Write clean, well-documented, and reproducible code — all analytical work should be re-runnable and reviewable by a peer without additional explanationParticipate actively in code review — both submitting work for review and reviewing peers' analytical code for methodological soundness and code qualityApply appropriate corrections for multiple comparisons, report effect sizes alongside p-values, and communicate uncertainty honestly in all deliverablesContribute to shared analytical frameworks, reusable templates, and reference implementations that raise the methodological standard across the teamEnsure all deliverables meet the COE's data product certification standards before releasePartnership with Cross-Functional Teams:Work closely with the business functions as the quantitative execution partner — receiving analytically defined questions and returning rigorous, well-documented findingsCommunicate statistical methodology, uncertainty, and analytical limitations clearly and, where needed, to non-technical audiences.Partner with Analytics Engineering to connect statistical output to the governed reporting layer — ensuring model scores, segments, and analytical results are operationalized correctlyParticipate in sprint ceremonies, peer reviews, and COE-wide delivery forums as an active contributorFrame high-value problems directly with senior business leaders, set the scientific standards for the data science practice, and mentor data scientists — operating self-sufficiently.Required Qualifications:PhD or Master’s degree in a quantitative field (statistics, mathematics, econometrics, computer science, or a related discipline) — required8+ years of experience in data science, statistical analysis, or applied quantitative research in a professional environmentStrong proficiency in Python for statistical and data science workloads: pandas, numpy, scipy, stats models, scikit-learnDeep working knowledge of statistical methodology: hypothesis testing, regression analysis, experimental design, causal inference, and Bayesian methodsDemonstrated experience applying advanced quantitative methods — survival analysis, time-series analysis, clustering, or simulation — to real business problemsHands-on experience with Databricks for data exploration, collaborative analysis, and notebook-based workflowsAbility to communicate statistical findings clearly and honestly to both technical peers and non-technical stakeholdersExperience working in a structured delivery environment with sprint cadences and cross-functional collaborationPreferred Qualifications:Experience in a regulated industry (life sciences, healthcare, financial services) where analytical methodology is subject to scrutiny or auditConsulting experience in data science and insights delivery roles.Familiarity with quasi-experimental and non-experimental causal inference methodsExperience contributing to model handoffs with an AI/ML engineering team — including documentation and validation supportExperience working in a global capability center (GCC) or center of excellence (COE) environment