Lead Data Scientist

Role Overview

We are hiring a Lead Data Scientist - Pharma Analytics to lead client-facing analytics engagements and deliver advanced AI/ML solutions for global pharmaceutical organizations.

The ideal candidate will possess deep expertise in Pharma Commercial Analytics, Marketing Analytics, Machine Learning, Advanced Analytics, and Data Science delivery, with strong hands-on experience in Python, SQL, Cloud technologies, and pharma datasets. The role requires balancing technical excellence, stakeholder management, and team leadership while translating complex business problems into scalable analytics solutions.

Key Responsibilities

Analytics & Solution Delivery

• Partner with business stakeholders and clients to understand business challenges and translate them into analytical solutions.
• Design and deliver scalable data science and advanced analytics solutions.
• Lead end-to-end analytics projects from problem definition through deployment and impact measurement.
• Develop predictive models, forecasting solutions, machine learning algorithms, and optimization frameworks.
• Drive insights generation through statistical modelling, machine learning, segmentation, forecasting, and performance analytics.
• Present actionable recommendations and storytelling-driven insights to senior client stakeholders.

Team Leadership

• Lead, mentor, and develop a team of Data Scientists and Analysts.
• Conduct technical solution reviews, code reviews, and mentoring sessions.
• Drive adoption of best practices across Data Science, AI/ML, Cloud, GenAI, and Analytics delivery.
• Ensure high-quality and timely project execution.

Client Engagement

• Collaborate closely with Pharma Commercial, Marketing, Brand, Sales, Market Access, and Strategy teams.
• Participate in solution design, capability building, proposal development, and client presentations.
• Build strong relationships with senior stakeholders and act as a trusted analytics advisor.

Required Skills (Must Have)

Core Technical Skills

• Advanced proficiency in Python
• Advanced proficiency in SQL
• Strong expertise in:
  • Machine Learning
  • Predictive Modeling
  • Statistics
  • Hypothesis Testing
  • Feature Engineering
  • Model Validation
  • Forecasting
• Data Visualization:
  • Power BI (Preferred)
  • Tableau (Alternative)
• Cloud exposure in at least one:
  • AWS
  • Azure
  • GCP

Skill Mix 1: Commercial Pharma Analytics

Mandatory Pharma Analytics Experience

Hands-on experience in one or more of:

• Commercial Analytics
• Sales Analytics
• Market Analytics
• Brand Analytics
• Performance Analytics
• Forecasting Analytics

Pharma Data Sources

Strong working knowledge of at least some of the following:

• IQVIA
• Symphony Health
• MMIT
• Truven
• HealthJump
• Prescription Data
• Claims Data
• Patient-Level Commercial Data

Preferred Use Cases

• Sales Force Effectiveness
• Territory Alignment
• Targeting & Segmentation
• Commercial Performance Measurement
• Incentive Compensation Analytics
• Launch Analytics
• Market Share Analytics

Skill Mix 2: Marketing Analytics & AI/ML

Mandatory Pharma Marketing Analytics Experience

Strong experience in commercial and marketing analytics within pharmaceutical or life sciences organizations.

Marketing Analytics Expertise

Hands-on experience in one or more of:

• Marketing Mix Modeling (MMM/MMX)
• Multi-Touch Attribution (MTA)
• Campaign Effectiveness
• Customer Segmentation
• Omnichannel Analytics
• Customer Journey Analytics
• Next Best Action (NBA)
• Forecasting Models

AI/ML Applications in Pharma

Experience in:

• Predictive Analytics
• Machine Learning
• Time Series Forecasting
• Recommendation Engines
• NLP Applications
• Customer Analytics
• Physician Analytics
• GenAI Solutions
• LLM-based Applications

Good to Have

• Generative AI and Agentic AI applications
• NLP and LLM-based Analytics
• Time Series Forecasting
• MLOps
• Databricks
• Snowflake
• Healthcare/Pharma Consulting experience
• Experience working with global pharmaceutical clients
• Exposure to advanced cloud-based ML deployments

Leadership Competencies

• Strong analytical and problem-solving mindset.
• Ability to independently drive client conversations.
• Proven stakeholder management skills.
• Experience mentoring and developing analytics teams.
• Ability to manage multiple projects simultaneously.
• Strong written and verbal communication skills.
• Ability to influence senior stakeholders through data-driven recommendations.

Preferred Educational Qualification

• Bachelor's or Master's degree in:
  • Statistics
  • Mathematics
  • Computer Science
  • Engineering
  • Data Science
  • Analytics
• MBA is an added advantage.