Senior Data Scientist

October 14, 2026

Remote

Senior

Warsaw, Poland

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Description

Softeq is looking for a Senior Data Scientist to build predictive and analytical models over operational and financial data. The role covers feature engineering on curated data layers, preparation of inputs for analytics products, and alignment of metric definitions with the semantic layer. The company is expanding the team and launching new projects within the next 1–2 months, with interviews starting for selected candidates.

Responsibilities

Explores operational and financial data, frames hypotheses and tests them
Builds predictive and analytical models: delays and durations, congestion, cost, plan-versus-actual variance
Engines features over the curated data layers and documents their meaning and provenance
Works with historical data states so that a training sample reflects what was known at the time of the event rather than what is known now
Prepares the inputs behind analytics products: KPI dashboards, user activity analytics, executive summaries, trend analysis
Aligns metric and calculation definitions with the semantic layer so figures agree across reports, APIs and interfaces
Validates results with business stakeholders and produces model documentation

Requirements

Analytical Python: pandas, scikit-learn, statistical libraries, clear visualization
Spark SQL and PySpark, confident work on large tables, window functions, and an understanding of distributed execution
Databricks as a working environment: notebooks, jobs, experiment tracking, catalog and data permissions, table versions and time travel
Understanding of the medallion model and which layer is the correct source for a given analysis
Statistics and time-series methods, sound experiment design, baselines and honest quality metrics
Experience with raw data from enterprise systems of record: gaps, duplicates, retroactive corrections, inconsistent reference data
Ability to explain and defend a result to a non-technical stakeholder
Storing features in the catalog and handing them to the production path
Transportation, logistics or supply chain
Geospatial analysis and route data
Experience handing a model over to an ML engineer for production

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