Applied Data Scientist job opportunity at Expereo.

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Date2026-02-02T12:05:50.558Z bot
Expereo Applied Data Scientist
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Pattern: Full-time
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loacation Katowice, Poland
loacation Katowice....Poland

We are the  Intelligent Internet Platform.  We connect  People, Places  and  Things  anywhere,  managing Internet Performance  better than anyone else, while providing  One Global Experience,  giving  Visibility, Control  and  Security  through  expereoOne. Expereo believes in the power of Internet connectivity. As the world's largest provider of managed internet, SD-WAN/SASE, and Cloud connectivity solutions, we power enterprises and government sites worldwide, helping to enhance every business' productivity with flexible and optimal Internet performance. As a trusted partner of Fortune 500 enterprises and carriers, our continued aim and success in helping both our customers and partners depends solely on the talented individuals that make Expereo a dynamic, effective, multicultural, and equitable environment.   About the role We are looking for an — Applied Data Scientist — to join the Digital & Innovation Office and work directly on the analytical and computational core of our platform. This is not a research role. It is not a dashboard role. And it is not an infrastructure role. You will write Python every day. You will work in Jupyter Notebooks to explore and validate ideas, then productized the logic that works. You will tackle constraint optimization problems in real-life business support tool stack (working with an existing Python/C# backend), build and validate pricing models, and extract insight from a growing data lake. The core of the role is the intersection of three things: Constraint solving and mathematical optimization — building deterministic, auditable logic that converts complex commercial requirements into reproducible quotes. Data analysis and pricing intelligence — working with supplier cost data, market signals, and historical outcomes to develop and validate value-based pricing models. Hands-on data engineering — writing the pipelines, transformations, and data quality checks that keep the platform’s analytical layer clean and usable. You will work directly with the Quote Engine lead and the Platform lead — two strong engineers — and report to the Chief Innovation Officer. The team is small, the codebase is real, and the problems are genuinely hard. Key Responsibilities Constraint Solving & Quote Engine Help design and implement constraint optimization logic for the quote engine: supplier selection, service bundling, routing rules, margin application. Use libraries such OR-Tools to model and solve real-world commercial optimization problems. Ensure all outputs are deterministic, auditable, and explainable to commercial stakeholders — this is not a black box. Validate model outputs against actual historical data; measure and report prediction reliability. Pricing Analysis & Value-Based Modelling Develop and iterate on value-based pricing models using supplier cost data, market benchmarks, and customer willingness-to-pay signals. Conduct exploratory data analysis in Jupyter Notebooks; surface patterns and validate hypotheses before productizing. Build systematic dashboards comparing predicted costs against actual budget data — measurable and independently verifiable outputs. Support the commercial team in understanding model outputs and building confidence in data-driven pricing decisions. Data Engineering & Pipeline Work Write Python pipelines to ingest, clean, and transform data from supplier systems, network telemetry, and Salesforce into the data lake. Implement data quality checks and automated validation logic as part of the pipeline, not as an afterthought. Work with the platform engineer on the existing data lake architecture — understand the structure, extend it, and keep it clean. Apply version control and code-review practices to all analytical work — notebooks that work locally but nowhere else are not deliverables. Collaboration & Communication Translate model logic and data findings into plain language for commercial, product, and operations stakeholders. Participate in sprint reviews, propose analytical approaches, and challenge assumptions with data. Be a partner to engineers, not a dependency — own your work end-to-end from exploration to production. Must-Have Advanced Python — non-negotiable. pandas, numpy, scikit-learn as daily tools. Writing clean, tested, reusable code, not just scripts. Constraint solving or mathematical optimization experience. Proven use of OR-Tools, or equivalent in a real-world setting. Strong data analysis skills. Jupyter Notebooks-native. Able to move from raw data to validated hypothesis to production logic. Experience with data pipelines and transformation. Writing ETL/ELT pipelines in Python, handling messy real-world data from multiple source systems. Git and engineering practices. Code lives in a repository, goes through review, and is reproducible without intervention. Ability to communicate analytical reasoning clearly to non-technical stakeholders. Strong Plus Experience with pricing models, revenue optimization, or commercial analytics in a B2B or telecom context. Familiarity with dbt, Airflow, Dagster, or similar orchestration/transformation tooling. Experience with AWS data services (S3, Glue, Athena, Redshift) or equivalent cloud data stack. Exposure to ML model deployment or MLOps practices (model registries, monitoring, inference pipelines). Familiarity with graph models, network topology data, or telco domain knowledge.

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