Lead Data Scientist

MathCo; Bengaluru; On-site; INR 4,000,000 - 8,000,000; Full time

Job summary

MathCo in Bengaluru, India is seeking a Lead Data Scientist for Pharma Analytics to lead client engagements and deliver advanced AI/ML solutions for global pharma clients. The role requires deep expertise in Pharma Commercial Analytics, Marketing Analytics, Machine Learning, Advanced Analytics, and Data Science delivery, with hands‑on Python, SQL, and cloud experience.

The role emphasizes balancing technical excellence with stakeholder management and team leadership, translating complex business

Qualifications

• Hands-on experience with pharma datasets and analytics delivery.
• Strong stakeholder management and team leadership.
• Proven ability to translate business problems into analytics solutions.
• Experience with predictive modeling and time series forecasting.
• Familiarity with GenAI, NLP, and cloud ML deployments.

Responsibilities

• 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.

Skills

Python

SQL

Cloud

Pharma analytics

Marketing analytics

ML

Education

Bachelor's or Master's in Statistics / CS / Engineering / Data Science

Tools

Power BI

AWS

Job description

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:
• Predictive Modeling
• Statistics
• Hypothesis Testing
• 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.

Preferred Educational Qualification:

• Bachelor's or Master's degree in: Statistics, Mathematics, Computer Science, Engineering, Data Science, Analytics