Python Data Scientist/Engineer

Location: Flexible (Hybrid in Wroclaw) Department: Front‑Office Technology & Analytics Type: ContractAbout the Role We’re working with a leading global investment organisation that is expanding its front‑office engineering function. This role sits at the intersection of data engineering, applied data science, and front‑office portfolio support, with a focus on building high‑quality Python systems that directly enable portfolio managers, analysts, and risk teams. You’ll work across several investment teams, helping to rewrite key components, productionise POCs, optimise data access patterns, and deliver reliable, scalable pipelines that power core analytics and proprietary models. If you enjoy solving complex data problems, building production systems end‑to‑end, and working closely with front‑office stakeholders, this is a high‑impact opportunity. What You’ll Work On • Design, build, and operate Python-based data pipelines (batch & near‑real‑time) reading/writing to governed enterprise storage (e.g., parquet patterns via internal cloud storage). • Develop production‑ready ML/data components including feature prep, scoring pipelines, and API‑exposed data services. • Support the re‑engineering and migration of legacy workflows into modern Python-based solutions. • Partner directly with front‑office portfolio managers and analysts to extend and industrialise proprietary models. • Optimise Databricks connectivity and collaborate with platform teams to improve Spark/PySpark workloads. • Support portfolio teams with data access, connectivity principles, and technical design for productionisation. • Contribute to engineering standards: CI/CD, testing, code reviews, observability, automation, and documentation. • Work on POCs through to full production rollout, including rewriting logic from other languages into Python where required. What We’re Looking For Must‑Have Skills • Strong, professional-level Python engineering skills (data structures, packaging, typing). • Experience with ETL or ML‑oriented Python workflows. • Experience building robust data pipelines and working with parquet/columnar formats in governed storage. • Exposure to PySpark or distributed data processing. • Comfortable working in platform environments (Databricks, containerised services, internal dev platforms). • Strong software engineering foundations: Git, testing, code review, CI/CD, documentation.