Lead Data Scientist

Lead Data Scientist – Pharma Analytics
Location: Bengaluru
Experience: 8-11 Years
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.

Work arrangement
No