Manager Data Science [T500-25836]

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.

About IACTO:

At ANSR MedTech, the Insights & Analytics (I&A) Center of Excellence is built as a single, integrated analytics organization with two closely partnered focus areas: Growth aligned analytics and CTO aligned analytics.

Both work together to ensure analytics drives real outcomes at enterprise scale.

Growth Aligned Insights & Analytics:

Growth aligned I&A teams focus on turning data into decisions.

They work closely with business leaders to understand context, analyze data, and deliver insights that directly inform commercial, product, and operational actions.

These roles are best suited for people who enjoy:

• Working close to the business and stakeholders
• Translating complex data into clear insights and recommendations
• Driving measurable impact through analytics.

CTO Aligned Insights & Analytics:

CTO aligned I&A teams focus on building and operating the data and AI foundation that enables analytics at scale.

They design, build, and maintain platforms, pipelines, models, and governance that ensure analytics is secure, reliable, and repeatable.

These roles are best suited for people who enjoy:

• Building scalable data platforms and AI capabilities
• Engineering high quality, production ready data solutions
• Establishing standards, reliability, and long-term sustainability.

How the Teams Work Together:

• Growth defines what insights matter and how they are used
• CTO enables how those insights are delivered at scale
• Teams operate as one connected analytics organization, with clear ownership and shared outcomes

This model allows ANSR MedTech to deliver fast, meaningful insights while building a strong, enterprise grade analytics foundation for long term growth.

Job Summary:

The Manager of Data Science will lead a team of data scientists within the India COE, accountable for the design and development of advanced analytics, statistical models, and predictive capabilities that drive business decision-making. This role is responsible for translating business problems into rigorous analytical approaches and ensuring that every model delivered meets enterprise-grade standards for quality, documentation, and reliability.

This role reports to the Director of Data & AI and operates within a multi-disciplinary delivery organization collaborating closely with analytics engineers, AI engineers, data engineers, and data governance professionals to deliver end-to-end analytical products.

Key Responsibilities:

• Lead the design and development of predictive models, statistical analyses, and advanced analytics solutions including propensity scoring, customer segmentation, churn analysis, demand forecasting, survival analysis, and cohort studies
• Establish rigorous model development methodology: hypothesis formulation, feature engineering, model selection, validation, and performance benchmarking
• Define and enforce model documentation standards including methodology, assumptions, limitations, variable importance, and interpretability requirements
• Define model performance requirements, acceptance thresholds, and retraining criteria. Hand over production-ready model artifacts and documentation to AI engineering for deployment, monitoring, and lifecycle management
• Develop advanced analytical capabilities including time-series forecasting, causal inference, optimization models, simulation frameworks, anomaly detection, clustering, and geospatial analysis
• Build and maintain experimentation infrastructure including A/B testing frameworks, holdout methodologies, and statistical significance standards
• Define measurement frameworks that connect model outputs to business outcomes ensuring every model has a clear value hypothesis and success criteria
• Establish standards for experiment design, sample sizing, power analysis, and result interpretation across the team
• Recruit, develop, and lead a team of data scientists at varying experience levels (associate through senior)
• Conduct regular code reviews, methodology reviews, and peer learning sessions to maintain quality and foster continuous improvement
• Define skill requirements for each level and create clear expectations for progression from associate to senior data scientist
• Foster a culture of scientific rigor, reproducibility, and intellectual honesty where assumptions are challenged and results are validated
• Define and enforce coding standards, peer review processes, quality gates, and definition-of-done criteria for data science deliverables consistent with how ANSR MedTechs established Data & AI teams operate
• Ensure all models are version-controlled, tested, documented, and certified through the data product certification process before production release
• Prepare and hand over model artifacts, feature pipelines, and technical documentation to AI engineering for production deployment following established AI engineering practices and standards
• Contribute to the COEs library of reusable analytical components, feature engineering patterns, and model templates
• Partner with Data Product Owners and Business Analysts to ensure deliverables align with business requirements and acceptance criteria
• Collaborate with peer managers across data science, analytics engineering, AI engineering, and data engineering to share standards and best practices
• Participate in sprint planning and capacity alignment with the I&A organization
• Contribute to cross-functional reviews of delivery metrics and business impact.

Qualifications:

• Masters degree or PhD in statistics, mathematics, computer science, economics, or a related quantitative field
• 712+ years of hands-on experience in data science, applied statistics, or quantitative analytics with at least 3 years leading technical teams
• Deep expertise in statistical modeling, machine learning, and experimental design with strong proficiency in at least two of: causal inference, Bayesian methods, time-series analysis, survival analysis, optimization, or deep learning
• Proficiency in Python and SQL; hands-on experience with Databricks, Spark, or equivalent distributed computing platforms
• Experience building and validating models through the full development lifecycle from hypothesis through feature engineering, training, evaluation, and handover to engineering for production deployment
• Strong understanding of feature engineering, model selection, regularization, cross-validation, and ensemble methods
• Ability to design and interpret experiments (A/B tests, multi-armed bandits, quasi-experimental methods) with statistical rigor
• Experience working in agile delivery teams with data engineers, analytics engineers, and product owners
• Excellent communication skills with the ability to present complex analytical findings to business stakeholders.

Preferred Skills:

• Experience in MedTech, life sciences, healthcare, or other regulated industries
• Familiarity with MLOps concepts and tooling (MLflow, feature stores, model registries) to ensure smooth handover to AI engineering
• Experience with deep learning frameworks (PyTorch, TensorFlow) for applied ML use cases
• Published research or conference presentations in applied data science or statistics
• Familiarity with Azure and Databricks ecosystems.