New opportunity

EY - GDS Consulting - AI And DATA -Life Sciences Commercial -Manager

EY · Bengaluru, India

About this role

The Manager for Life Sciences Commercial AI & Data Analytics will lead client engagements that leverage pharmaceutical commercial, real-world, and digital data to shape strategy and execution. This role requires a blend of deep life sciences domain expertise, analytics leadership, and practical knowledge of AI and modern data platforms to design fit-for-purpose solutions and guide multidisciplinary teams.

Responsibilities

Lead analytics engagements addressing market opportunity, patient journeys, segmentation, targeting, omnichannel performance, launch readiness, field effectiveness, forecasting, and brand performance.
Frame ambiguous client challenges into clear hypotheses, analytical workplans, and decision points.
Guide the selection and interpretation of pharmaceutical commercial and real-world data, ensuring appropriate metrics and limitations.
Identify high-value applications of machine learning and generative AI, directing the design and validation of solutions like propensity modeling, next-best-action, and NLP.
Ensure responsible AI practices, including transparency, human oversight, and compliance with regulatory expectations.
Guide teams in designing scalable analytical datasets and robust ETL/ELT pipelines across various data sources like claims, EHR/EMR, and CRM.
Synthesize analytical findings into strategic recommendations and lead senior stakeholder discussions.
Lead, coach, and review multidisciplinary teams while contributing to proposals and practice development.

Required qualifications

7–12 years of experience in life sciences commercial analytics, healthcare analytics, analytics consulting, or data science.
Significant experience with pharmaceutical datasets such as IQVIA, Komodo, Symphony, Optum, HealthVerity, TriNetX, EHR/EMR, claims, and CRM data.
Strong understanding of the US pharmaceutical commercial model and global market dynamics.
Proven leadership in commercial use cases including patient analytics, segmentation, and forecasting.
Experience shaping analytics solutions using SQL, Python/R, BI tools, and cloud data platforms.
Experience directing data integration, engineering, and model validation for machine learning or generative AI solutions.
Bachelor’s or Master’s degree in life sciences, engineering, data science, business, or a related discipline.

Preferred qualifications

MBA or advanced degree in pharma, healthcare, or analytics.
Experience supporting US or global pharmaceutical commercial teams.
Experience in oncology or other complex specialty therapeutic areas.
Experience contributing to consulting proposals or analytics products.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Life Sciences Commercial AnalyticsHealthcare AnalyticsData ScienceMachine LearningGenerative AISQLPythonRBI ToolsCloud Data PlatformsETL/ELTPharmaceutical DataPatient AnalyticsSegmentation and TargetingOmnichannel MeasurementMarketing MixField EffectivenessForecastingLaunch AnalyticsMarket AssessmentResponsible AIConsulting

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Commercial Analytics Strategy Leadership
The manager must lead engagements across the pharmaceutical commercial lifecycle, including patient journeys, segmentation, and forecasting. This requires translating ambiguous client business questions into structured analytical workplans and actionable strategic recommendations.
AI and Data Solution Architecture
The role requires directing the design of scalable analytical datasets and robust ETL/ELT pipelines using diverse pharmaceutical data sources like claims, EHR, and CRM. You must oversee the validation of machine learning and generative AI models while ensuring compliance with responsible AI practices.
Multidisciplinary Team and Stakeholder Management
You will lead and coach diverse teams of data scientists, engineers, and analysts. A core priority is acting as a trusted advisor to senior stakeholders, synthesizing complex analytical findings into clear business narratives and managing project delivery risks.

Likely challenges to think through

Bridging Technical Complexity and Business Utility
A significant challenge is ensuring that advanced AI/ML models remain interpretable and relevant to commercial business stakeholders. Prepare by demonstrating how you explain model assumptions, limitations, and trade-offs to non-technical executives without sacrificing analytical rigor.
Navigating Data Heterogeneity and Quality
Pharmaceutical commercial data (e.g., IQVIA, Komodo, EHR) is often fragmented and complex. You will face the challenge of maintaining data lineage and quality across disparate sources. Prepare by highlighting your experience in setting data governance standards and managing complex integration projects.
Balancing Innovation with Regulatory Constraints
Implementing generative AI and advanced analytics in a highly regulated life sciences environment requires strict adherence to compliance and privacy. Prepare by discussing your approach to embedding responsible AI frameworks, human oversight, and documentation into the development lifecycle.

Practice your answers

Technical practice

Pharmaceutical Data Integration Strategy

What to coverExplain how you would approach integrating disparate datasets like claims, EHR/EMR, and CRM data for a patient journey analysis. Focus on your methodology for handling data lineage, quality controls, and the creation of reusable analytical data models.

Machine Learning in Commercial Use Cases

What to coverDescribe your experience designing propensity models or next-best-action solutions for pharmaceutical sales teams. Discuss how you validate these models and ensure they provide actionable insights rather than just predictive scores.

Responsible AI and Governance

What to coverHow do you ensure transparency and regulatory compliance when deploying generative AI or machine learning models in a life sciences context? Address the importance of human oversight, model documentation, and monitoring for bias.

Translating Ambiguous Business Challenges

What to coverWalk through your process for framing an ambiguous client request, such as 'improving brand performance,' into a structured analytical workplan. Explain how you define hypotheses, success metrics, and decision points.

ETL/ELT Pipeline Design

What to coverDiscuss the trade-offs between ETL and ELT approaches when working with large-scale cloud data platforms in a consulting environment. How do you ensure these pipelines remain robust and scalable for complex healthcare data?

Forecasting and Market Assessment

What to coverWhat are the common pitfalls when building forecasting models for pharmaceutical products? Explain how you incorporate market dynamics and real-world data limitations into your forecasting methodology.

Omnichannel Performance Measurement

What to coverHow do you measure the effectiveness of omnichannel marketing strategies in the pharmaceutical industry? Discuss the metrics you prioritize and how you attribute impact across different digital and field-based touchpoints.

Technical Leadership and Review

What to coverAs a manager, how do you review the technical work of data scientists and engineers to ensure high standards of analytical rigor? Provide an example of how you challenge solution designs without stifling innovation.

Behavioural practice

Managing Senior Stakeholder Expectations

What to coverDescribe a situation where you had to explain complex model limitations or trade-offs to a non-technical senior stakeholder. How did you ensure they understood the business implications while maintaining their trust?

Leading Multidisciplinary Teams

What to coverProvide an example of how you have coached a team with diverse skill sets (e.g., data engineers, data scientists, and domain experts) to deliver a complex project. How did you manage roles, timelines, and potential conflicts?

Driving Business Value in Consulting

What to coverTell me about a time you identified an adjacent client need during an engagement that led to expanded work or a new proposal. How did you articulate the value of this solution to the client?

Handling Project Risks and Priorities

What to coverDescribe a time when a project faced significant delivery risks or competing priorities. How did you assess the situation, communicate with the client, and adjust your strategy to ensure a successful outcome?

Sharpen your knowledge

Topics to prepare

Pharmaceutical Commercial Ecosystem
Understand the US pharmaceutical commercial model, including patient and HCP journeys, launch dynamics, and market access. Exercise: Map out a typical patient journey for a specialty therapeutic area (e.g., oncology) and identify the specific data touchpoints (claims, EHR, CRM) where analytics can influence commercial strategy.
Responsible AI and Model Governance
Focus on the ethical deployment of machine learning and generative AI in healthcare, emphasizing transparency, human oversight, and regulatory compliance. Exercise: Draft a framework for validating a propensity model, specifically outlining how you would document model assumptions, potential biases, and performance monitoring protocols for a non-technical stakeholder.
Consulting Problem Structuring
Develop the ability to translate ambiguous client challenges into clear hypotheses and analytical workplans. Exercise: Take a hypothetical client request like 'improve field effectiveness' and break it down into three distinct, testable hypotheses, defining the necessary data sources and success metrics for each.

Skills to revise

Pharmaceutical Data Integration
Proficiency in handling complex datasets like IQVIA, Komodo, and claims/EHR data within ETL/ELT pipelines. Exercise: Design a high-level data model that integrates longitudinal patient claims data with CRM interaction logs to support an omnichannel performance analysis.
Advanced Analytics Leadership
Directing multidisciplinary teams in applying SQL, Python, or R to solve commercial problems. Exercise: Review a sample Python script for a segmentation analysis and identify three areas where you would improve the code for scalability, reproducibility, and alignment with enterprise data standards.
Executive Storytelling
Synthesizing complex analytical findings into strategic recommendations for senior stakeholders. Exercise: Prepare a 3-slide executive summary based on a hypothetical forecasting project, focusing on translating model outputs into actionable business decisions and risk mitigations.

Your preparation checklist

Prepare a structured case study on pharmaceutical commercial analytics
Develop a concise walkthrough of a past project where you translated an ambiguous business challenge (e.g., declining brand performance or launch readiness) into a clear analytical workplan. Focus on how you selected specific datasets like claims or EHR, defined success metrics, and communicated strategic recommendations to senior stakeholders.
Map your experience to specific pharmaceutical data ecosystems
Create a summary table of your hands-on experience with industry-standard datasets such as IQVIA, Komodo, Symphony, or Optum. For each, be prepared to discuss the specific business questions they answer, their inherent limitations, and how you have managed data quality or lineage issues in previous roles.
Formulate a framework for responsible AI in life sciences
Prepare to discuss your approach to implementing machine learning or generative AI solutions. Specifically, outline how you ensure transparency, human oversight, and regulatory compliance when deploying models like propensity scoring or next-best-action engines in a commercial pharmaceutical context.
Draft a technical leadership narrative for multidisciplinary teams
Prepare examples of how you have guided data engineers and data scientists in designing scalable ETL/ELT pipelines and analytical data models. Emphasize your ability to bridge the gap between technical architecture and business-driven outcomes, including how you review code and ensure delivery quality.
Prepare thoughtful questions for the interviewers
Develop questions that demonstrate your commercial acumen, such as: 'How is the team currently balancing the integration of generative AI with existing legacy commercial analytics workflows?' or 'What are the primary challenges the team faces when scaling analytical solutions across different global therapeutic areas?'

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