Data Science Manager

Biopharma Careers; Hyderabad; On-site; INR 2,500,000 - 4,200,000; Full time

Job summary

Amgen is seeking a seasoned data science professional in Hyderabad to build and maintain predictive models identifying high-potential healthcare professionals. The role emphasizes collaboration with brand, sales, analytics, and IT teams to ensure models are scalable, explainable, and deployed into business workflows.

You will mentor data scientists, drive feature engineering and model evaluation, and translate complex analytics into actionable business recommendations for a fast-paced biopharma

Qualifications

• 7+ years in predictive modeling, ML, and commercial analytics.

Responsibilities

• Build and maintain models to identify and prioritize HCPs by treatment behavior, opportunity, and engagement.

Skills

Predictive modeling

Machine learning

Commercial analytics

Python

SQL

Databricks

PySpark

Feature engineering

Model evaluation

Explainable ML

Business translation

Tools

Databricks

PySpark

Job description

Career Category

Engineering

Job Description

HOW MIGHT YOU DEFY IMAGINATION?

If you feel like you’re part of something bigger, it’s because you are. At Amgen, our shared mission, to serve patients, drives all that we do. It is key to our becoming one of the world’s leading biotechnology companies. We are global collaborators who achieve together, researching, manufacturing, and delivering ever-better products that reach over 10 million patients worldwide. It’s time for a career you can be proud of.

Live | What you will do

• Build and maintain models that identify and prioritize HCPs based on factors such as treatment behavior, patient opportunity, prescribing patterns, referral dynamics, engagement history, channel responsiveness, and likelihood to act.
• Provide technical guidance and mentorship to data scientists, ensuring best practices in feature engineering, model development, validation, measurement, reproducibility, and business interpretation.
• Partner with brand, sales, field operations, global analytics, data engineering, and technology teams to ensure HCP models are business-relevant, scalable, explainable, and deployable into downstream systems.

Thrive | What you can expect

• As we work to develop treatments that take care of others, we also work to care for our teammates’ professional and personal growth and well-being.
• You will be part of a collaborative analytics environment where data science is used to improve commercial decision‑making, strengthen customer engagement, and help teams better understand patient and HCP needs.

Basic Qualifications

• 7 years of hands‑on experience in predictive modeling, machine learning, and commercial analytics
• Demonstrated experience building predictive models for customer targeting, scoring, prioritization, and segmentation.
• Strong programming skills in Python and SQL, with experience working in Databricks, PySpark, or similar big data environments.
• Strong understanding of feature engineering, model validation, model performance evaluation, explainability, and business translation.
• Ability to convert ambiguous commercial questions into structured analytics problems, modeling approaches, and actionable recommendations.
• Experience with explainable ML techniques and the ability to communicate key model drivers to non‑technical business stakeholders.
• Strong ownership mindset, structured problem‑solving ability, and bias toward action in a fast‑paced business environment.

Preferred Qualifications

• Experience in life sciences, pharma, biotech, or regulated healthcare analytics environments.
• Experience working with large‑scale healthcare and commercial datasets, such as claims, EMR, lab, Rx, patient longitudinal data, CRM activity, sales, call activity, and digital engagement data.
• Deep understanding of HCP analytics, including targeting, prioritization, segmentation, field force optimization, omnichannel engagement, patient opportunity sizing, and treatment journey analytics.
• Experience working with IT teams to deploy models into production or business workflows
• Exposure to advanced methods such as uplift modeling, causal inference, graph/network analytics, and reinforcement learning, in commercial data science.