Manager – AI & Business Intelligence Analytics - Pharma/LifeSciences

Job Description: AI & Self-Service Analytics

Total Exp - 6 years - 12 Years

Overview

Analytics Function Overview

The Analytics function focuses on enabling next-generation Business Intelligence through AI-driven insights, self-service analytics, and conversational reporting capabilities for leading global pharmaceutical organizations. The function partners closely with Commercial, Medical, Market Access, and Executive stakeholders to scale insight delivery while reducing manual reporting effort.

The AI & Self-Service Analytics team plays a critical role in operationalizing AI-assisted analytics, natural language interaction with data, and persona-aligned self-service datasets within enterprise BI platforms such as Tableau, Power BI, and cloud data platforms.

Roles and Responsibilities Summary

Design and deliver AI-enabled reporting and self-service analytics solutions aligned to pharma commercial and medical use cases

Support implementation of conversational BI capabilities using tools such as Power BI Copilot, Tableau Pulse, and in-house LLM frameworks

Translate business questions into AI-assisted insight workflows (e.g., variance explanation, trend detection, anomaly identification)

Build and curate persona-specific self-service datasets for Analysts, Data Scientists, and Advanced Business Users

Collaborate with BI Architects and Semantic Layer teams to ensure AI readiness of data products (grain, metadata, KPI logic)

Configure and validate natural language query patterns mapped to governed KPIs and dimensions

Develop automated KPI narratives and explanations to support faster decision-making

Partner with business stakeholders to identify high-value AI use cases for reporting and analytics (e.g., field performance insights, payer mix shifts, launch signals)

Support UX design for AI interactions, including prompt design, guided queries, and explainability views

Ensure AI outputs are transparent, explainable, auditable, and compliant with governance guidelines

Contribute to training materials and enablement content for AI-driven and self-service analytics adoption

Take ownership of assigned deliverables and support program milestones under guidance of Group Managers / Leads

Core Competencies

Technical Skills

Power BI (Copilot, AI visuals, semantic models)

Tableau (Tableau Pulse, metric insights, guided analytics)

SQL for analytics-ready data validation

Understanding of semantic layers, KPI definitions, and governed datasets

Exposure to cloud data platforms (Snowflake, Databricks preferred)

Familiarity with LLM concepts, embeddings, prompt design, and AI explainability

Domain & Functional Knowledge

Working knowledge of pharma commercial analytics (sales performance, field effectiveness, market access, omnichannel)

Understanding of common pharma datasets (CRM activity, sales, claims, payer, digital engagement)

Ability to frame business questions into AI-assisted analytics patterns

Behavioral & Professional Skills

Strong stakeholder communication (verbal and written)

Structured problem-solving and analytical thinking

Ability to work with cross-functional global teams (business, IT, data governance)

Attention to detail with a quality-focused mindset

Adaptability to evolving AI and analytics platforms

Collaborative and team-oriented approach

Must-Have Skills

Power BI (including Copilot exposure) or Tableau (including Tableau Pulse)

SQL and analytical data validation skills

Strong communication and documentation skills

Experience supporting self-service analytics or advanced BI users

Understanding of KPI-driven reporting and semantic models

Problem-solving and structured thinking

Good-to-Have Skills

Exposure to conversational analytics or NLP-based querying

Experience working with AI explainability or insight generation features

Knowledge of pharma therapy areas or launch analytics

Familiarity with Databricks, Python, or AI notebooks

Experience supporting global / multi-region BI programs

Highest Education

Bachelors or Masters degree in Engineering, Computer Science, Data Analytics, Statistics, or related quantitative field

Strong academic background with exposure to analytics, data, or AI coursework