Sr. Data Scientist

Amgen Technology; Hyderabad; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Amgen Technology in Hyderabad seeks a Senior Data Scientist to build and maintain predictive models that identify key HCPs and opportunities in pharmaceutical analytics. You will guide a team of data scientists, ensuring robust feature engineering, validation, and business interpretation.

Collaboration with brand, sales, and IT teams will ensure models are scalable and explainable. The role emphasizes translating complex commercial questions into actionable analytics, with a strong ownership

Qualifications

• 7 years hands-on experience in predictive modeling and analytics.
• Proficient in Python and SQL; Databricks and PySpark experience.
• Ability to translate ambiguous business questions into structured analytics problems.

Responsibilities

• Build and maintain models to identify and prioritize HCPs based on various factors.
• Provide technical guidance and mentorship to data scientists, ensuring best practices in modeling and interpretation.
• Partner with brand, sales, analytics, and IT teams to deploy models into production or downstream systems.

Skills

Predictive modeling

Machine learning

Commercial analytics

Python

SQL

Databricks

PySpark

Feature engineering

Model validation

Explainable ML

Business translation

Problem solving

Tools

Databricks

PySpark

Job description

HOW MIGHT YOU DEFY IMAGINATION

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.