ANSR
Website:
ansr.com
Job details:
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.
The Director, Applied AI owns the Applied AI solution delivery capability for the CTO-aligned Insights & Analytics organization. This leader identifies and shapes AI opportunities, translates business problems into AI product requirements, and leads cross-functional delivery from concept through production deployment and value realization. The role is accountable for AI products, copilots, agents, decision engines, workflow automation, Responsible AI governance, and adoption outcomes. The Director manages and partners closely with Data Science, Platform AI Engineering, Data Engineering, Analytics Engineering, Security, and Data Governance teams to deliver enterprise-scale AI solutions.
Key Responsibilities:
AI Portfolio & Value Realization:
- Build and maintain a value-ranked Applied AI portfolio aligned to I&A priorities. Partner with I&A Commercial team to define business outcome metrics and manage AI demand pipeline, prioritization, and executive reviews.
AI solution design and delivery:
- Frame AI-ready problem statements, success metrics, user journeys, data readiness, change impacts, and operating-model implications.
- Establish intake, triage, prioritization, and stage-gate practices for AI use cases, including proof-of-value, pilot, production, scale, and retirement decisions.
- Lead design and delivery of applied AI products including copilots, agents, predictive decision engines, recommendation systems, workflow automation, and generative AI experiences.
- Define solution patterns for retrieval-augmented generation, prompt orchestration, tool use, human-in-the-loop workflows, model selection, evaluation, and integration with business applications.
- Convert data science and research outputs into usable AI products with clear product requirements, acceptance criteria, UX/workflow design, operational handoffs, and adoption plans.
AI Product Management:
- Define product vision, roadmap, success measures, and release strategy for AI-enabled capabilities. Prioritize product backlog based on business impact and user feedback. Ensure adoption and business process integration.
Data Science:
- Design, build, and deploy predictive models, customer segmentation frameworks, propensity scoring, churn analysis, and statistical models that drive business decision-making
- 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
LLMOps, MLOps, and production readiness:
- Partner with Platform AI Engineering to operationalize model, prompt, agent, and retrieval pipelines with CI/CD, evaluation automation, deployment gates, observability, rollback, and incident response.
- Define production readiness standards for latency, reliability, scalability, cost, data quality dependencies, model performance, drift, prompt/version control, and service-level expectations.
- Ensure every deployed AI solution has monitoring, feedback loops, outcome measurement, ownership, support model, and a continuous-improvement roadmap.
Responsible AI, risk, and compliance:
- Embed Responsible AI by design across the lifecycle, including use-case risk tiering, privacy and security review, fairness and bias considerations, explain ability, human oversight, content safety, and regulatory alignment.
- Create required AI governance artifacts such as AI impact assessments, model cards, prompt/system documentation, evaluation reports, approval records, audit trails, and release readiness checklists.
- Implement continuous controls for AI quality and safety, including test suites, red-teaming, hallucination and toxicity checks, data leakage checks, monitorable guardrails, escalation paths, and periodic risk reviews.
AI Data Readiness & Quality:
- Implement Data quality requirements for AI solutions, Dataset certification for AI use, Readiness assessments for AI deployment.
- Enable data product certification framework: the quality, documentation, and reliability standards every analytical deliverable must meet before production release
Leadership & organization building:
- Build and lead a high-performing applied AI team across applied AI product management, AI solution architecture, prompt/agent engineering, ML/LLM engineering, evaluation engineering, and AI delivery leadership.
- Establish communities of practice, reusable design patterns, delivery playbooks, and quality standards that accelerate responsible AI delivery across the organization.
- Coach teams to balance speed, experimentation, quality, safety, and enterprise-grade sustainability.
- 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
- Enforce 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
- Implement data quality validation framework that certifies datasets before any analytics product is built on them, and own the data product certification process: the quality, documentation, and reliability bar every deliverable must clear before production release
Platform & infrastructure governance:
- Partner with Platform AI Engineering to ensure AI solutions can be deployed, monitored, scaled, and operated reliably.
- Define Applied AI requirements for experimentation environments, model hosting, vector stores, agent frameworks, orchestration, and monitoring.
- Contribute to platform roadmap prioritization based on Applied AI use cases.
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
Qualifications:
- 12+ years of progressive experience across AI/ML, data science, digital product delivery, analytics, or technology leadership, with 5+ years in people leadership or large cross-functional delivery leadership roles.
- Proven track record translating ambiguous business problems into production AI products or intelligent workflows with measurable business impact.
- Deep understanding of AI/ML, generative AI, LLM/agent architectures, RAG, experimentation, evaluation, model risk, MLOps/LLMOps, and product adoption practices.
- Experience embedding Responsible AI, privacy, security, compliance, and risk controls into AI delivery lifecycles, preferably in regulated environments.
- Ability to influence senior stakeholders, make tradeoffs across value, feasibility, risk, and adoption, and communicate complex AI concepts in business terms.
Preferred Skills:
- Experience in MedTech, life sciences, healthcare, or another regulated industry.
- Experience scaling AI products in a GCC, COE, or global matrixed operating model.
- Hands-on familiarity with Azure, Databricks, Microsoft AI services, modern data platforms, ML platforms, and enterprise application integration patterns.
- Experience with AI product management, design thinking, change management, and value-realization measurement.
- Master’s degree or PhD in computer science, statistics, engineering, mathematics, data science, or a related quantitative discipline.
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