Senior I Data Scientist

Softeq; Warszawa; On-site; PLN 120,000 - 190,000; Full time

Job summary

Softeq in Warsaw is seeking a data analytics engineer to explore operational and financial data, build predictive models, and prepare analytics products such as KPI dashboards and trend analyses.

The role focuses on Python data science, Spark SQL, and Databricks in a collaborative environment with cross-functional stakeholders. You will validate results and document methodologies, contributing to production-ready pipelines.

Qualifications

• Analytical Python: pandas, scikit-learn, statistical libraries, clear visualization
• Spark SQL and PySpark, large tables, window functions, distributed execution
• Databricks notebooks, jobs, experiment tracking, catalog and data permissions
• Understanding of medallion model and source layers for analysis
• Statistics and time-series methods, experiment design and 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

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
• Engineers 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
• 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

Skills

Python (Pandas)

Spark SQL

PySpark

Databricks

Time-series analysis

Statistics

Data visualization

Tools

Databricks

SQL

Job description

8 days ago Requisition ID: 1095

Established in 1997, Softeq was built from the ground up to specialize in new product development and R&D, tackling the most difficult problems in the tech sphere. Now we've expanded to offer early-stage innovation and ideation plus digital transformation business consulting. Our superpower is to deliver all of this under one roof on a global scale. So let's get started and build a better future together!

As we're expecting to expand our team and launch new projects within the next 1-2 months, we're already accepting applications and starting the interview process for selected candidates. We'd love to hear from you.

What the role does here

• Explores operational and financial data, frames hypotheses and tests them;
• Builds predictive and analytical models: delays and durations, congestion, cost, plan-versus-actual variance;
• Engineers 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.

Must have

• 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.

Nice to have

• 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.

Softeq communicates only from @ softeq.com email addresses. We never request payments or fees for any reason during hiring - including trainings or courses to be completed, equipment, onboarding, or background checks - and we will not ask for banking information, cryptocurrency or gift cards. If you receive a message from any other domain or requesting payment, do not respond and report it to [e-mail]