New opportunity

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

EY · Bengaluru, India

About this role

The Senior Consultant in Life Sciences Commercial AI & Data Analytics works with global EY teams and clients to address commercial pharmaceutical challenges across brand strategy, omnichannel engagement, customer analytics, product launch, field effectiveness, and patient analytics. The role combines deep pharmaceutical commercial domain knowledge with practical analytics, AI, and data-engineering capabilities to develop datasets, predictive models, interactive dashboards, and actionable business recommendations.

Responsibilities

Analyze pharmaceutical commercial and real-world data to evaluate market opportunity, customer segmentation, patient journeys, omnichannel performance, launch readiness, and brand performance.
Assess data sources for feasibility and define populations, cohorts, business rules, metrics, and study variables.
Build analytical datasets by integrating, transforming, cleaning, and validating claims, EHR/EMR, lab, specialty pharmacy/HUB, CRM, sales, and digital engagement data.
Develop analytical frameworks, perform exploratory and statistical analyses, and execute modeling using SQL, Python, or R.
Apply machine learning and AI techniques for segmentation, propensity, next-best-action, forecasting, anomaly detection, NLP, and insight generation.
Support responsible generative AI implementation, including prompt design, output validation, human review, and data protection.
Develop and maintain modular SQL/Python data pipelines, ingestion processes, and quality controls using cloud and ETL/ELT platforms.
Build commercial reporting dashboards in Power BI or Tableau and automate recurring workflows.
Translate analytical outputs into commercial insights, executive presentations, and strategic narratives for marketing, sales, and market access teams.
Collaborate with global teams and clients, manage workstream timelines, and mentor junior analysts.

Required qualifications

3-7 years of experience in life sciences commercial analytics, healthcare analytics, analytics consulting, data science, or a related field.
Hands-on experience with pharmaceutical datasets such as IQVIA, Komodo, Symphony, Optum, HealthVerity, TriNetX, EHR/EMR, claims, SP/HUB, laboratory, CRM, sales, or digital engagement data.
Understanding of the US pharmaceutical commercial model and familiarity with global markets, key stakeholders, patient/HCP journeys, and regulatory/privacy requirements.
Experience delivering commercial use cases such as patient analytics, segmentation, targeting, omnichannel measurement, marketing mix, or forecasting.
Hands-on proficiency with SQL and working knowledge of Python or R.
Experience with Power BI or Tableau and familiarity with cloud data platforms and ETL/ELT tools (e.g., Azure Data Factory, AWS Glue, Databricks, Snowflake, Talend).
Exposure to machine learning, generative AI, or advanced analytics in a business context with knowledge of model validation.
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, analytics, or a related field.
Experience supporting US or global pharmaceutical commercial teams.
Experience in oncology or another complex specialty therapeutic area.
Experience contributing to consulting proposals, reusable accelerators, 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

SQLPythonRMachine LearningGenerative AIPower BITableauCommercial AnalyticsHealthcare AnalyticsData ScienceReal-World DataETLDatabricksSnowflakeAzure Data FactoryAWS GlueTalendIQVIAKomodoSymphonyOptumHealthVerityTriNetXEHR/EMRPrompt DesignCustomer SegmentationForecastingNatural Language Processing

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

Healthcare and Real-World Data Engineering
Directly ingest, validate, and integrate heterogeneous pharmaceutical data assets such as IQVIA, Komodo, Optum, EHR/EMR, and claims. The role requires building modular SQL and Python pipelines that transform disparate raw datasets into clean, analysis-ready structures while enforcing rigorous data-quality and cohort-definition rules.
Commercial AI and Advanced Predictive Modeling
Apply machine learning, advanced analytics, and responsible generative AI to address commercial use cases including patient journey analytics, HCP targeting, segmentation, forecasting, and omnichannel effectiveness. Emphasis is placed on selecting appropriate models and validating business relevance over unjustified complexity.
Translation of Analytics into Commercial Strategy
Convert quantitative findings into executive-ready narratives, Power BI/Tableau dashboards, and actionable recommendations for brand, launch, marketing, and market access teams. The role demands connecting data patterns directly to pharmaceutical market dynamics, treatment pathways, and customer behaviors.
Cross-Functional Consulting and Team Enablement
Collaborate closely with global EY teams and client stakeholders across multiple time zones to scope ambiguous business questions, align on deliverables, and manage workstream timelines, while actively mentoring junior analysts on technical execution and domain practices.

Likely challenges to think through

Handling Incomplete and Disjointed Real-World Datasets
Real-world data such as claims, specialty pharmacy/HUB, and EHR feeds frequently suffer from missingness, coding discrepancies, and latency, which can distort patient cohort analyses. Prepare by practicing structured approaches to cohort curation, survival analysis handling, and clear communication of data limitations to non-technical stakeholders.
Balancing Advanced Modeling with Commercial Interpretability
Commercial pharmaceutical leaders require explainable drivers for decisions such as sales force targeting or marketing mix rather than black-box models. Prepare case studies demonstrating how to evaluate whether simple statistical models or complex ML/GenAI solutions best serve the business need without sacrificing interpretability.
Managing Distributed Global Delivery and Time-Zone Coordination
Operating out of GDS Bengaluru while serving US and global pharmaceutical stakeholders requires reconciling asynchronous delivery expectations, differing cultural contexts, and tight client timelines. Prepare by formulating clear frameworks for scoping ambiguous requests, setting upfront data assumptions, and orchestrating workstreams across time zones.

Practice your answers

Technical practice

How do you evaluate data fitness and construct longitudinal patient cohorts across fragmented claims and EHR datasets?

What to coverExplain the differences between closed and open claims datasets (such as IQVIA or Komodo) versus clinical EHR systems, focusing on enrollment gaps and variable coding. Describe specific business rules and SQL methods used to establish an index date, continuous eligibility, and washout windows. Address how you validate cohorts against potential attrition and selection biases.

When developing HCP segmentation or next-best-action models, how do you decide between machine learning and simpler statistical models?

What to coverDiscuss the explicit trade-off between predictive uplift and business interpretability required by commercial field teams. Detail how you evaluate model performance using metrics such as ROC-AUC, lift charts, or calibration curves against commercial ROI. Explain how you defend choosing simpler rule-based or regression methods when high explainability is required.

How do you build modular, production-ready SQL and Python pipelines for recurring commercial analytics reporting?

What to coverDescribe the architecture of repeatable data transformations across cloud platforms such as Databricks or Snowflake. Highlight practices for unit testing, schema enforcement, deduplication, and automated data quality checks on large-scale health data. Mention version control, containerization, or DAG orchestration strategies as an inferred preparation suggestion for pipeline maintainability.

What frameworks and safeguards do you apply when integrating generative AI into commercial pharmaceutical workflows?

What to coverAddress the role's explicit focus on responsible GenAI, prompt design, output validation, human-in-the-loop review, and data privacy. Detail specific safeguards needed to prevent leakage of protected health information (PHI) or proprietary brand strategy. Explain how you evaluate hallucination risks and ensure auditability in LLM-assisted insight generation.

How would you design a commercial brand performance dashboard in Power BI or Tableau to track a new product launch?

What to coverDetail the selection of core commercial metrics such as TRx/NRx velocity, patient persistency (PDC/MPR), conversion rates, and sales rep call activity. Explain data modeling best practices in the BI tool to handle multi-fact tables and high-cardinality patient or prescriber keys efficiently. Discuss how you tailor interactive views to differentiate executive strategic summaries from granular brand-team diagnostics.

How do you integrate specialty pharmacy or HUB services data with standard syndicated commercial feeds?

What to coverDescribe the operational differences between HUB data (e.g., patient onboarding, prior authorization, bridge programs) and broad syndicated claims (e.g., Symphony, Optum). Detail the technical approach to tokenized patient linking, handling data latency, and deduplication across multiple specialty pharmacies. Outline how you calculate drop-off rates and time-to-first-fill metrics across these sources.

How do you measure marketing mix and omnichannel engagement effectiveness across healthcare professional touchpoints?

What to coverDiscuss methodology for attributing brand performance across field rep details, digital non-personal promotion (NPP), email campaigns, and webinars. Address analytical challenges such as correlation among marketing channels, lag effects, and diminishing returns on promotional spend. Explain how you translate statistical attribution findings into actionable channel reallocation recommendations for brand managers.

What approaches do you take to identify anomalies and ensure data quality in inbound healthcare feeds?

What to coverOutline methods for detecting data shifts, such as sudden drops in claim volume, missing NDCs, or changes in payer coverage policies. Explain how you automate validation rules using SQL or Python scripts to catch data anomalies before analytical datasets are generated. Address how you communicate data discrepancies to third-party data vendors and client technical counterparts.

Behavioural practice

Describe a situation where you had to translate complex analytical findings into actionable recommendations for non-technical commercial leaders.

What to coverStructure your answer using the STAR format, focusing on a scenario involving brand or commercial stakeholders. Detail how you distilled technical model outputs into clear commercial drivers, risks, and strategic next steps. Emphasize how you handled non-technical skepticism and ensured client adoption of your recommendations.

Give an example of managing conflicting priorities or tight delivery deadlines across geographically distributed teams.

What to coverProvide a real project scenario involving cross-time-zone collaboration with global stakeholders and delivery teams. Explain how you scoped ambiguous requirements, established priority workstreams, and managed risks or dependencies. Highlight the outcome in terms of deliverable quality, client satisfaction, and project continuity.

Describe a time when you identified a critical data quality issue or analytical flaw right before a major client deliverable.

What to coverUse the STAR approach to illustrate your attention to detail and rigorous quality mindset in a high-stakes consulting environment. Detail the specific actions you took to investigate the root cause, communicate transparently with leadership, and remediate the issue under time pressure. Emphasize the preventative quality controls or reusable checks implemented as a result.

How have you mentored a junior analyst to improve their technical execution and healthcare domain acumen?

What to coverDescribe a specific instance where you guided a junior team member on coding practices, SQL/Python data handling, or pharmaceutical context. Explain your approach to balancing constructive code reviews with teaching business storytelling and domain nuances. Detail the measurable improvement in the analyst's autonomy, deliverable quality, or confidence.

Sharpen your knowledge

Topics to prepare

Pharmaceutical Commercial Datasets and Real-World Evidence Structures
Directly required by the posting (IQVIA, Komodo, Symphony, Optum, HealthVerity, TriNetX, claims, and EHR/EMR). Review core schemas, transaction mechanics, diagnosis/procedure coding (ICD-10, CPT, NDC), and closed versus open claims limitations. Practice exercise: Diagram a longitudinal patient journey mapping from initial diagnosis through prescription fill, identifying typical data gaps, lag times, and specialty pharmacy/HUB drop-offs.
Pharma Commercial Use Cases and Business Metrics
The posting emphasizes commercial challenges such as patient analytics, HCP segmentation, targeting, omnichannel measurement, field effectiveness, and product launch readiness. Study how brand performance metrics (TRx, NRx, market share, persistency, compliance, conversion rate) are calculated. Practice exercise: Walk through an end-to-end framework to assess HCP adoption and next-best-action channel targeting for a newly launched specialty drug.
Machine Learning and Advanced Analytics in Commercial Life Sciences
The role requires applying ML/AI for segmentation, propensity scoring, next-best-action, forecasting, and anomaly detection, emphasizing model validation and explaining limitations over unnecessary complexity. Practice exercise: Contrast a Random Forest or XGBoost propensity-to-switch model with a logistic regression baseline, outlining how to evaluate calibration, feature importance, and commercial interpretability for non-technical brand managers.
Responsible Generative AI and Governance in Life Sciences
The description explicitly highlights responsible generative AI, prompt design, output validation, human review, and sensitive information protection. Review prompt engineering workflows, hallucination mitigation, and data privacy safeguards when processing life sciences data. Practice exercise: Draft a governance and evaluation protocol for a GenAI assistant summarizing clinical notes or market research while safeguarding HIPAA/PII compliance.

Skills to revise

Advanced SQL for Cohort Construction and Metric Aggregation
Explicitly required for building analytical datasets, defining study-specific variables, and applying business rules across large healthcare tables. Practice exercise: Write modular SQL queries using window functions (e.g., ROW_NUMBER, LAG/LEAD) and CTEs to identify patient index dates, calculate 90-day medication adherence (PDC/MPR), and flag line-of-therapy progression.
Data Pipeline and Modeling Implementation in Python or R
Required for exploratory data analysis, pipeline automation, and statistical modeling. Practice exercise: Write a Python script using pandas and scikit-learn (or R equivalent) to clean multi-source claims and CRM engagement data, handle missing values, build an HCP segmentation model, and export summary metrics with repeatable logging.
Commercial Dashboard Design in Power BI or Tableau
Explicitly required to deliver dashboards and automate recurring reporting workflows for commercial stakeholders. Practice exercise: Design a wireframe dashboard layout displaying brand launch KPIs, brand performance vs. competitors, and field force omnichannel interactions, detailing data model relationships and drill-down metrics.
Cloud Data Platform and ETL/ELT Architecture Review
The posting lists familiarity with cloud platforms and ETL tools (Azure Data Factory, AWS Glue, Databricks, Snowflake, Talend). Inferred preparation suggestion: Review how data lakehouse patterns (e.g., Databricks Delta tables or Snowflake stages) optimize ingestion, curation, and transformation of large-scale healthcare data feeds.

Your preparation checklist

Prepare an end-to-end commercial pharmaceutical project walkthrough
Develop a structured narrative of a past project centered on a key commercial use case (e.g., patient journey analytics, HCP targeting, or launch tracking). Outline the specific healthcare data assets utilized (e.g., IQVIA, claims, or EHR), data cleaning challenges, statistical or ML modeling choices, and the measurable business impact delivered to commercial stakeholders.
Conduct a mock healthcare cohort design and SQL data modeling exercise
Draft sample SQL scripts demonstrating advanced cohort definition logic on simulated longitudinal claims/EMR tables. Include clean implementations of index dates, pre-index baseline windows, post-index follow-ups, and adherence metrics (such as PDC or MPR) to demonstrate fluency in real-world data transformation rules.
Synthesize a case study comparing machine learning versus simple commercial analytics
Formulate a concrete example illustrating when advanced ML (e.g., propensity scoring or next-best-action models) was warranted versus when simpler regression or rule-based models better answered client needs. Be ready to articulate trade-offs regarding explainability, validation metrics, and commercial adoption by sales or brand teams.
Structure a responsible generative AI and governance framework for life sciences
Prepare an outline covering practical prompt engineering, automated output evaluation, human-in-the-loop validation, and sensitive data safeguards (HIPAA/PHI) when deploying GenAI tools for commercial narrative generation or unstructured medical text synthesis.
Draft behavioral scenarios highlighting cross-border collaboration and junior mentoring
Prepare 2-3 STAR-format examples demonstrating how you scoped ambiguous analytical requirements with global teams across time zones, resolved data quality bottlenecks under tight deadlines, and coached junior team members on coding standards and commercial storytelling.
Formulate strategic questions for the EY GDS interviewers
Prepare 3-4 thoughtful questions focused on team delivery dynamics, such as the standard cloud and data architecture stack (e.g., Databricks, Snowflake) used across global engagements, how EY balances reusable IP development with bespoke client projects, and current client adoption of GenAI in commercial analytics.

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