Director, Insights and Analytics - CTO T2

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 scale.

Job Summary:

The Director of Data & AI will own the end-to-end technical delivery capability for the India COE, including all technical talent, methodology, quality standards, platform governance, and career development. This role is the single point of accountability for how analytics, data science, AI, and data engineering work is designed, built, deployed, and evolved - ensuring enterprise-grade quality, consistency, and scalability across ANSR MedTech's global footprint.

Key Responsibilities:

Data Science:Design, build, and deploy predictive models, customer segmentation frameworks, propensity scoring, churn analysis, and statistical models that drive business decision-makingEstablish experimentation infrastructure including A/B testing frameworks, holdout methodologies, and statistical significance standardsDevelop advanced analytical capabilities including survival analysis, time-series forecasting, causal inference, and optimization modelsImplement model performance monitoring, drift detection, and retraining pipelines to ensure production models maintain accuracy over timeDefine model documentation standards including methodology, assumptions, limitations, and interpretability requirements Analytics engineering:Build and maintain enterprise dashboards, operational reporting, executive scorecards, and self-service analytics layers that enable data-driven decision-making across business functionsDesign and implement the semantic layer - standardized business logic, metric definitions, and governed data models that ensure consistent reporting across teams and geographiesDevelop data transformation pipelines that convert raw source data into analytics-ready datasets with documentation, testing, and version controlCreate and maintain a self-service enablement program: governed datasets, curated data products, and training materials that empower business users to explore data independentlyEstablish visualization standards, accessibility guidelines, and information design principles for all COE-produced reporting AI engineering:Build and operate machine learning infrastructure including feature stores, model registries, training pipelines, and serving endpointsImplement MLOps practices: automated model training, validation, deployment, monitoring, and rollback across development, staging, and production environmentsDesign and maintain production-grade inference pipelines that serve model predictions at the latency, throughput, and reliability levels required by business applicationsDevelop reusable ML components, pipeline templates, and accelerators that reduce time-to-production for new AI use casesCollaborate with data scientists to translate research-grade models into production-ready, scalable, and maintainable systems Data engineering:Architect, build, and operate the data platform infrastructure on Azure and Databricks, ensuring it meets the performance, scalability, and cost requirements of the India COEDesign and implement data ingestion frameworks covering batch, streaming, and event-driven patterns across internal and external data sourcesBuild and maintain data lake and data warehouse architectures with clear separation of bronze, silver, and gold data layers following medallion architecture principlesDevelop data pipeline orchestration, monitoring, alerting, and incident response capabilities that ensure production data reliabilityDefine and enforce data engineering standards including code review, testing, CI/CD, documentation, and operational runbooks Data governance:Establish and operationalize data quality standards: profiling rules, completeness thresholds, accuracy checks, and automated monitoring that validate data before it is used for analytics or AIBuild and maintain the enterprise metadata layer: business glossary, data catalog, data lineage tracking, and impact analysis capabilities for all COE-managed data assetsImplement a federated data stewardship model where business stakeholders define data rules and KPI logic while the technical team enforces them through automation and toolingDefine the data product certification framework: the quality, documentation, and reliability standards every analytical deliverable must meet before production releaseOwn data classification, retention policies, and compliance documentation for all COE-managed datasets Leadership & organization building:Build the technical team from the ground up across all five disciplines, phased to match the COE's maturity trajectoryDefine and execute the talent acquisition strategy - identifying the right mix of experience levels, technical specializations, and leadership capabilities at each growth phaseEstablish structured career progression paths for each technical track, creating clear advancement trajectories that attract and retain top talent in a competitive India marketCreate a technical community of practice that drives cross-pollination between disciplines, shared standards, peer learning, and a culture of engineering excellenceOwn performance management, mentoring, coaching, and professional development for all technical staffFoster a delivery culture centered on quality, accountability, and continuous improvement - consistent with how ANSR MedTech's established Data & AI teams operate globally Delivery methodology & technical standards:Define and enforce a unified delivery methodology across all squads - consistent with how ANSR MedTech's established Data & AI teams operateEstablish coding standards, peer review processes, quality gates, and definition-of-done criteria for every deliverable across all five disciplinesImplement automated testing, continuous integration, and deployment practices for data pipelines, analytics products, and ML modelsDefine the data quality validation framework that certifies datasets before any analytics product is built on themOwn the data product certification process: the quality, documentation, and reliability bar every deliverable must clear before production releaseCreate and maintain reusable blueprints, templates, and design patterns that reduce delivery time and ensure consistency as the team scales Platform & infrastructure governance:Architect and govern the data platform (Azure, Databricks) for the India COE, ensuring alignment with the global enterprise technology stackDefine the technology cost framework: separate baseline infrastructure from incremental COE-driven demand with transparent allocation and quarterly reviewEstablish platform engineering practices including environment management, capacity planning, cost optimization, and infrastructure-as-codeEnsure all platform decisions support long-term scalability, multi-tenancy, and operational efficiency as the COE grows Stakeholder partnership:Partner with business stakeholders to ensure delivery priorities are aligned with business needs and that capacity is allocated against the highest-value workParticipate in regular cross-functional reviews of delivery metrics, business impact, and pipeline healthContribute to strategic planning with senior leadership on COE performance, capability expansion, and long-term roadmapBuild trusted relationships with business stakeholders, technology partners, and vendor ecosystems (Databricks, Microsoft, AWS) Qualifications:14+ years of progressive experience in data science, analytics engineering, data engineering, or AI/ML, with at least 5 years in people leadership rolesProven track record of building and scaling technical teams from early stage through organizational maturityDeep expertise in at least two of the following: data science, analytics engineering, AI/ML engineering, data platform architecture, data governanceWorking proficiency with modern cloud-native data platforms (Databricks, Azure, Snowflake, or equivalent)Experience establishing data governance frameworks including data quality, metadata management, lineage, and stewardshipStrong understanding of agile delivery methodology applied to data and analytics teams . click apply for full job details