Job Summary

We are seeking a Manager in Life Sciences Commercial AI & Data Analytics to lead client engagements that use pharmaceutical commercial, real-world, and digital data to shape strategy and execution.

The role combines deep life sciences domain expertise with analytics leadership, client advisory, and practical knowledge of AI and modern data platforms. The Manager will frame complex business questions, design fit-for-purpose analytics and data solutions, guide multidisciplinary teams, and translate evidence into decisions across brand strategy, omnichannel engagement, customer analytics, launch, field effectiveness, and patient analytics.

The ideal candidate will be a credible commercial analytics advisor who can connect pharmaceutical market dynamics with data, technology, and delivery realities ensuring that solutions are analytically sound, scalable, compliant, and useful to business stakeholders.

Key Responsibilities

- Lead Commercial Analytics Strategy and Delivery
- Lead analytics engagements addressing market prospect, patient journeys, segmentation and targeting, omnichannel performance, launch readiness, field effectiveness, forecasting, and brand performance.
- Frame ambiguous client challenges into clear hypotheses, analytical workplans, success measures, and decision points.
- Guide the selection and interpretation of pharmaceutical commercial and real-world data, ensuring that populations, cohorts, business rules, metrics, and limitations are appropriate to the business question.
- Own delivery quality, timelines, risks, and executive-ready outputs across multiple workstreams.

- Shape AI and Advanced Analytics Solutions

- Identify high-value applications of machine learning, generative AI, and advanced analytics across commercial functions, and distinguish viable opportunities from technology-led experimentation.
- Direct the design and validation of solutions such as segmentation, propensity, next-best-action, forecasting, anomaly detection, NLP, and AI-assisted insight generation.
- Ensure responsible AI practices, including appropriate data use, transparency, human oversight,



performance monitoring, documentation, and compliance with client and regulatory expectations.
- Explain model assumptions, limitations, and business trade-offs to senior stakeholders in accessible language.

- Provide Data and Solution Architecture Direction

- Guide teams in designing scalable analytical datasets, reusable data models, and robust ETL/ELT pipelines across claims, EHR/EMR, laboratory, SP/HUB, CRM, sales, access, and digital sources.
- Set expectations for data quality, lineage, metadata, reproducibility, security, and documentation, and oversee resolution of complex data issues.
- Partner with data engineers, architects, data scientists, and visualization specialists to align solution design with analytical needs and enterprise technology standards.
- Promote reusable assets, modular code, automation, and cloud-based delivery approaches that improve quality and speed across engagements.

- Advise Clients and Translate Evidence into Action

- Act as a trusted advisor to commercial, marketing, sales, market access, analytics, and technology stakeholders.
- Synthesize analytical findings into clear strategic implications, recommendations, and implementation priorities.
- Lead workshops and senior stakeholder discussions, challenge assumptions constructively, and build alignment around decisions.
- Ensure recommendations reflect pharmaceutical market dynamics, treatment pathways, customer needs, regulatory constraints, and operational feasibility.

- Lead Teams and Build the Practice

- Lead, coach, and review multidisciplinary teams, creating clarity on roles, standards, timelines, and expected outcomes.
- Develop team capability across life sciences domain knowledge, analytics methods, AI,



data engineering, quality assurance, and business storytelling.
- Contribute to proposals, solution design, thought leadership, reusable accelerators, and the development of EY offerings.
- Support account growth by identifying adjacent client needs and articulating differentiated, value-led solutions.

Experience
- 712 years of experience in life sciences commercial analytics, healthcare analytics, analytics consulting, data science, or a related field, including significant responsibility for client and team leadership.
- Deep experience with pharmaceutical datasets such as IQVIA, Komodo, Symphony, Optum, HealthVerity, TriNetX, EHR/EMR, claims, SP/HUB, laboratory, CRM, sales, market access, and digital engagement data.
- Strong understanding of the US pharmaceutical commercial model and familiarity with global markets, including commercial functions, patient and HCP journeys, therapeutic areas, launch dynamics, and regulatory and privacy considerations.
- Proven leadership across multiple commercial use cases such as patient analytics, segmentation, targeting, omnichannel measurement, marketing mix, field effectiveness, forecasting, launch analytics, or market assessment.
- Experience shaping and overseeing analytics solutions using SQL, Python/R, BI tools, and modern cloud data platforms; sufficient technical depth to review solution design and challenge delivery teams.
- Experience directing data integration and engineering work, including analytical data models, ETL/ELT pipelines, data-quality controls, lineage, and reusable delivery assets.
- Experience designing or governing machine-learning or generative-AI solutions, including model validation, adoption, monitoring, and responsible AI considerations.
- Demonstrated experience managing senior client relationships, multidisciplinary teams, project economics, delivery risks, and competing priorities.

Domain and Consulting Skills
- Deep pharmaceutical commercial and healthcare domain expertise, with credibility in senior business discussions.

📌 EY - GDS Consulting - AI And DATA -Life Sciences Commercial -Manager (Bengaluru)
🏢 EY
📍 Bengaluru