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-making
- Establish experimentation infrastructure including A/B testing frameworks, holdout methodologies, and statistical significance standards
- Develop advanced analytical capabilities including survival analysis, time-series forecasting, causal inference, and optimization models
- Implement model performance monitoring, drift detection, and retraining pipelines to ensure production models maintain accuracy over time
- Define 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 functions
- Design and implement the semantic layer — standardized business logic, metric definitions, and governed data models that ensure consistent reporting across teams and geographies
- Develop data transformation pipelines that convert raw source data into analytics-ready datasets with documentation, testing, and version control
- Create and maintain a self-service enablement program: governed datasets, curated data products, and training materials that empower business users to explore data independently
- Establish 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 endpoints
- Implement MLOps practices: automated model training, validation, deployment, monitoring, and rollback across development, staging, and production environments
- Design and maintain production-grade inference pipelines that serve model predictions at the latency, throughput, and reliability levels required by business applications
- Develop reusable ML components, pipeline templates, and accelerators that reduce time-to-production for new AI use cases
- Collaborate 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 COE
- Design and implement data ingestion frameworks covering batch, streaming, and event-driven patterns across internal and external data sources
- Build and maintain data lake and data warehouse architectures with clear separation of bronze, silver, and gold data layers following medallion architecture principles
- Develop data pipeline orchestration, monitoring, alerting, and incident response capabilities that ensure production data reliability
- Define 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 AI
- Build and maintain the enterprise metadata layer: business glossary, data catalog, data lineage tracking, and impact analysis capabilities for all COE-managed data assets
- Implement a federated data stewardship model where business stakeholders define data rules and KPI logic while the technical team enforces them through automation and tooling
- Define the data product certification framework: the quality, documentation, and reliability standards every analytical deliverable must meet before production release
- Own 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 trajectory
- Define and execute the talent acquisition strategy — identifying the right mix of experience levels, technical specializations, and leadership capabilities at each growth phase
- Establish structured career progression paths for each technical track, creating clear advancement trajectories that attract and retain top talent in a competitive India market
- Create a technical community of practice that drives cross-pollination between disciplines, shared standards, peer learning, and a culture of engineering excellence
- Own performance management, mentoring, coaching, and professional development for all technical staff
- Foster 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 operate
- Establish coding standards, peer review processes, quality gates, and definition-of-done criteria for every deliverable across all five disciplines
- Implement automated testing, continuous integration, and deployment practices for data pipelines, analytics products, and ML models
- Define the data quality validation framework that certifies datasets before any analytics product is built on them
- Own the data product certification process: the quality, documentation, and reliability bar every deliverable must clear before production release
- Create 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 stack
- Define the technology cost framework: separate baseline infrastructure from incremental COE-driven demand with transparent allocation and quarterly review
- Establish platform engineering practices including environment management, capacity planning, cost optimization, and infrastructure-as-code
- Ensure 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 work
- Participate in regular cross-functional reviews of delivery metrics, business impact, and pipeline health
- Contribute to strategic planning with senior leadership on COE performance, capability expansion, and long-term roadmap
- Build 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 roles
- Proven track record of building and scaling technical teams from early stage through organizational maturity
- Deep expertise in at least two of the following: data science, analytics engineering, AI/ML engineering, data platform architecture, data governance
- Working proficiency with modern cloud-native data platforms (Databricks, Azure, Snowflake, or equivalent)
- Experience establishing data governance frameworks including data quality, metadata management, lineage, and stewardship
- Strong understanding of agile delivery methodology applied to data and analytics teams
- Experience operating in a matrixed environment with stakeholders across business and technology functions
- Excellent communication skills with the ability to influence senior leadership and translate technical concepts for non-technical audiences
Preferred Skills:
- Experience in medtech, life sciences, healthcare, or other regulated industries
- Experience building and operating a global capability center (GCC) or center of excellence (COE)
- Familiarity with FDA-regulated data environments and compliance requirements
- Master's degree or PhD in a quantitative field (computer science, statistics, mathematics, engineering, or related discipline)
- Experience defining and implementing technology cost governance frameworks for cloud-based data platforms.
