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.

Job Title: Principal Data Scientist

Department: IACTO

Function: CTO

Sub-Function: Insights and Analytics

Location: Bengaluru, India 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. About the Role:

The Principal Data Scientist will be a core technical contributor within the India COE's data science practice, responsible for designing and executing rigorous statistical analyses, developing quantitative models, and translating complex data into clear, defensible insights that inform business decision-making. This role sits at the intersection of statistical methodology and applied data science — the expectation is deep quantitative fluency, not just tooling familiarity.

The Data

Scientist works in close partnership with the Insights & Analytics (I&A;) function, which surfaces business questions and defines analytical priorities, and with Analytics Engineering and AI Engineering to ensure outputs are reproducible, governed, and actionable. 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 I&A; 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 easy 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 Quality & Reproducibility:

Write clean, well-documented,



and reproducible code — all analytical work should be re-runnable and reviewable by a peer without additional explanation Participate actively in code review — both submitting work for review and reviewing peers' analytical code for methodological soundness and code quality Apply appropriate corrections for multiple comparisons, report effect sizes alongside p-values, and communicate uncertainty honestly in all deliverables Contribute to shared analytical frameworks, reusable templates, and reference implementations that raise the methodological standard across the team Ensure all deliverables meet the COE's data product certification standards before release Partnership with I&A; and Cross-Functional Teams:

Work closely with the I&A; function as the quantitative execution partner — receiving analytically defined questions and returning rigorous, well-documented findings Communicate statistical methodology, uncertainty, and analytical limitations clearly to I&A; and, where needed, to non-technical audiences — without oversimplifying the findings Partner with Analytics Engineering to connect statistical output to the governed reporting layer — ensuring model scores, segments, and analytical results are operationalized correctly Participate in sprint ceremonies, peer reviews, and COE-wide delivery forums as an active contributor Required Qualifications:

10-12 years of experience in data science, statistical analysis, or applied quantitative research in a professional environment Robust proficiency in Python for statistical and data science workloads: pandas, numpy, scipy, statsmodels, scikit-learn Deep working knowledge of statistical methodology: hypothesis testing, regression analysis, experimental design, causal inference, and Bayesian methods Demonstrated experience applying advanced quantitative methods — survival analysis, time-series analysis, clustering, or simulation — to real business problems Hands-on experience with Databricks for data exploration, team-oriented analysis, and notebook-based workflows Ability to communicate statistical findings clearly and honestly to both technical peers and non-technical stakeholders Experience working in a structured delivery environment with sprint cadences and cross-functional collaboration Preferred Qualifications:

Experience in a regulated industry (life sciences, healthcare, financial services) where analytical methodology is subject to scrutiny or audit Master's degree or PhD in a quantitative field (statistics, mathematics, econometrics, computer science, engineering, or related discipline) Experience partnering with an Insights & Analytics, strategy, or business intelligence function as the quantitative methods layer Familiarity with quasi-experimental and non-experimental causal inference methods Experience contributing to model handoffs with an AI/ML engineering team — including documentation and validation support Experience working in a global capability center (GCC) or center of excellence (COE) workplace

📌 Principal Data Scientist-28234] (Bengaluru)
🏢 ANSR
📍 Bengaluru