- Company Name
- Eurobase People
- Job Title
- Data Scientist
- Job Description
-
**Job Title:** Data Scientist
**Role Summary:**
Full‑stack data scientist responsible for designing, implementing, and operationalizing data pipelines, machine‑learning (ML), and optimization models for an operations‑decision support product. Works within a cross‑functional squad, delivering end‑to‑end solutions that integrate with product tech, user interfaces, and business processes, while following agile and DevOps practices.
**Expectations:**
- Deliver production‑ready ML/optimization models in Python.
- Build robust ETL pipelines and integrate with orchestration tools such as Dagster or Airflow.
- Deploy models in a cloud environment using CI/CD workflows.
- Conduct data analyses, visualisation, and ROI assessment of decision‑support features.
- Collaborate with stakeholders for requirements, feedback, and prioritisation.
- Maintain code quality via Git best practices, automated tests, and documentation.
**Key Responsibilities:**
1. Analyse business problems end‑to‑end and identify decision‑support opportunities.
2. Prototype advanced ML and optimisation models, demonstrating use‑case value.
3. Develop production‑grade Python code (strict typing, classes, testing).
4. Create automated, resilient data cleaning pipelines.
5. Integrate core algorithms with workflow orchestration (e.g., Dagster).
6. Deploy models to cloud (AWS preferred) using CI/CD (GitHub Actions, SageMaker, etc.).
7. Implement logging, error handling, unit/integration/regression tests.
8. Harden models against edge cases and operational variations.
9. Quantify feature adoption and value‑capture; provide insights.
10. Engage with stakeholders for requirements, feedback, and roadmap discussions.
11. Contribute to agile squad processes, code reviews, documentation, and prioritisation.
**Required Skills:**
- Python fluency; strong use of scikit‑learn, pandas, numpy, seaborn.
- Proficiency in ML (supervised & unsupervised) and operations research (linear, MILP, heuristics).
- Experience with SQL and Python for data engineering.
- Cloud platform (AWS) knowledge; familiarity with SageMaker, Docker, ECS.
- Version control (Git), experiment tracking (MLflow), model & data versioning (DVC).
- Workflow orchestration (Airflow or Dagster).
- CI/CD (GitHub Actions, etc.) and automated testing (unit, integration).
- Advanced Excel, PowerPoint, and Microsoft Office.
- Strong analytical, communication, and collaborative skills.
**Required Education & Certifications:**
- Bachelor’s degree (or higher) in Computer Science, Data Science, Mathematics, Engineering, or a related field.
- Relevant certifications in machine learning, data engineering, or cloud platforms are advantageous.