Senior Data Scientist, Commercial Data Products

EPAM Systems; København; Hybrid; DKK 900,000 - 1,300,000; Full time

Job summary

EPAM Systems in Copenhagen offers a hybrid role focused on delivering AI-enabled data products for enterprise CDP teams. You will leverage Dataiku, Snowflake, and Power BI to optimize commercial operations, pre-call intelligence, and cross-team collaboration in a governed enterprise setting.

The position emphasizes building, deploying, and monitoring ML models with a strong emphasis on business impact and scalable data solutions across product and commercial stakeholders.

Qualifications

• 5+ years of experience as a Data Scientist in a commercial enterprise.
• Hands-on ML model building and deployment in Dataiku (or similar).
• Strong Python and SQL skills.
• Solid grounding in statistics and ML fundamentals.
• Comfortable in governed enterprise environments.
• Healthcare, claims, HCP, support-program, or CRM data experience (preferred).
• Life sciences / pharma commercial analytics background (preferred).

Responsibilities

• Design, build, and validate ML models for Commercial Optimization and Pre-Call Intelligence use cases.
• Consume features from the Feature Store (Dataiku/Snowflake); flag gaps back to Data Engineering.
• Perform exploratory data analysis, feature selection, and model experimentation in Dataiku.
• Train, tune, and evaluate models.
• Deploy and monitor models in production via Dataiku.
• Translate model outputs into business-ready insights with product and commercial stakeholders.
• Collaborate with Data Engineering, product, and delivery teams.

Skills

Python

SQL

ML fundamentals

Statistics

Model deployment

Enterprise governance

Healthcare/CRM data

Tools

Dataiku

Job description

EPAM Systems in Copenhagen offers a hybrid role focused on delivering AI-enabled data products for enterprise CDP teams. You will leverage Dataiku, Snowflake, and Power BI to optimize commercial operations, pre-call intelligence, and cross-team collaboration in a governed enterprise setting.

The position emphasizes building, deploying, and monitoring ML models with a strong emphasis on business impact and scalable data solutions across product and commercial stakeholders.