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Open nowPosted 31 hours ago

Data Scientist - Commercial Data Products (Optimization & Pre-Call Intelligence)

EPAM Systems

Where
Denmark
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Your applicationOpen nowData Scientist - Commercial Data Products (Optimization & Pre-Call Intelligence)EPAM Systems · Denmark

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Share of postings closed within
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  2. 4.2%3 days
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  4. 17.5%14 days
  5. 34.2%30 days
  6. 43.5%60 days
This job: posted 31 hours ago

The posting

You will drive the development of commercial data products for enterprise CDP teams, leveraging AI-enabled engineering excellence and cloud-based innovation labs (Snowflake). In this hybrid role based in Copenhagen, you will use Dataiku, Snowflake, and Power BI to optimize core commercial operations and pre-call intelligence. You will collaborate with engineering and product teams to deliver scalable solutions that improve productivity and quality while reducing long-term costs. This position anchors you in a managed delivery and optimization program, combining technical depth and cross-team partnership.

**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

**Requirements

**

  • At least 5 years experience as a Data Scientist in a Commercial Enterprise context
  • Hands-on ML model building and deployment in Dataiku (or similar)
  • Strong Python and SQL
  • 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)

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