Lead Data Scientist

PeopleStrong; India; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

PeopleStrong is seeking a Lead Data Scientist – Pharma Analytics to lead client-facing analytics engagements and deliver advanced AI/ML solutions for global pharmaceutical organizations. The role requires deep expertise in Pharma Commercial Analytics, Marketing Analytics, Machine Learning, and Data Science delivery, with strong hands-on experience in Python, SQL, and cloud technologies.

The ideal candidate will balance technical leadership with stakeholder management, team mentorship, and

Qualifications

• Hands-on experience in Commercial Analytics, Market Analytics, Brand Analytics, Performance Analytics, and Forecasting Analytics.
• Strong experience in Pharma Marketing Analytics including MMM/MMX, MTA, NBA.
• Experience applying AI/ML in Pharma with predictive analytics, time series forecasting, and NLP.
• Proven ability to lead client engagements and mentor analytics teams.
• Experience with cloud deployments (AWS/Azure/GCP) and GenAI solutions.

Responsibilities

• Lead client-facing analytics engagements and deliver AI/ML solutions for global pharma clients.
• Design and deliver scalable data science and advanced analytics solutions.
• Manage end-to-end analytics projects from problem definition to deployment and impact measurement.
• Develop predictive models, forecasting solutions, and optimization frameworks.
• Drive insights through statistical modelling, ML, segmentation, and performance analytics.
• Present actionable recommendations to senior stakeholders and drive adoption across teams.
• Mentor and develop a team of Data Scientists and Analysts.

Skills

Python

SQL

Machine Learning

Forecasting

Statistics

Hypothesis Testing

Feature Engineering

Model Validation

Power BI

Cloud technologies

Tools

Databricks

Snowflake

Job description

Pharma - Commercial Analytics Marketing Analytics Machine Learning Patient Flow Forecasting Data Science Customer Segmentation Strategy Claims Analysis & Capital Modelling Python SQL

Job Description

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.
• 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
• Power BI (Preferred)
• 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
• Market Analytics
• Brand Analytics
• Performance Analytics
• Forecasting Analytics

Pharma Data Sources

Strong working knowledge of at least some of the following:

• IQVIA
• MMIT
• HealthJump
• Prescription Data
• Claims Data

Preferred Use Cases

• Territory Alignment
• 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)
• Customer Journey Analytics
• Next Best Action (NBA)

AI/ML Applications in Pharma

Experience in:

• Predictive Analytics
• Time Series Forecasting
• Recommendation Engines
• NLP Applications
• Customer 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
• Strong analytical and problem-solving mindset.
• Ability to independently drive client conversations.
• 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.