Data Scientist Manager

Project & Technical Delivery - Lead the execution of client-facing data science, analytics, and data engineering projects from scoping through deployment and handover. - Design analytical approaches and solution architectures using our low-code data science studio, visual ETL platform, and enterprise lakehouse (with change-data-capture capabilities). - Write Python to extend, integrate, or bridge these platforms — custom transformations, model logic, APIs, automation, and anything the visual tools can't handle out of the box. - Build and operate the harder engineering layers of a solution when projects demand it: orchestrated pipelines, worker-based and event-driven architectures, stream processing, and scaling for data volume and concurrency. - Own solution quality: data validation, model evaluation, reproducibility, performance, and operational reliability (monitoring, alerting, failure recovery). Team Leadership - Lead and mentor a team of junior data scientists and analysts; review their work, unblock them technically, and grow their skills. - Break projects down into workable tasks, assign them across the team, and keep delivery on track. - Set and uphold standards for code quality, documentation, and analytical rigor. Stakeholder & Client Management - Serve as the primary technical point of contact for client stakeholders during project delivery. - Translate ambiguous business problems into well-defined analytical work, and communicate findings and trade-offs clearly to non-technical audiences. - Manage scope, timelines, and expectations; escalate risks early and propose options. Internal Collaboration - Work alongside our internal product engineering team (Python/FastAPI backend, React frontend) to feed field learnings back into the product and, occasionally, contribute to backend integrations. Qualifications & Experience - 5+ years of experience in data science and analytics, with at least 1 to 2 years leading or mentoring junior team members. - Proven project delivery record: scoping, planning, stakeholder management, and shipping solutions on time. Hard Skills & Knowledge - Strong, practical Python skills including pandas, NumPy, scikit-learn, API integration, and writing maintainable code others can build on. - Familiarity with Python backend frameworks such as FastAPI, Django, or Flask. - Solid grounding in statistics and machine learning, able to choose the right method for the problem. - Proficiency in SQL and comfort with large datasets on modern data platforms, including lakehouse architectures, columnar stores, and CDC-based ingestion. - Hands-on experience building and operating orchestration pipelines using Airflow, Dagster, Prefect, or equivalent, including scheduling, retries, backfills, and monitoring. - Working knowledge of distributed compute frameworks like Spark or Dask and sound judgement on when to use them versus simpler approaches. - Preferred experience: AI engineering exposure such as LLM applications, RAG pipelines, prompt engineering, or model serving. - Preferred experience: distributed workers and task queues (e.g., Celery, RQ), stream processing (e.g., Kafka, Flink, Spark Structured Streaming), low-code/visual ETL tools, React/frontend literacy, and containerised deployments with CI/CD. - Consulting or client services experience is a plus. Soft Skills & Qualities - Excellent communication skills with both technical teams and business stakeholders. - Strong analytical problem-solving and pragmatic decision-making under ambiguity. - Collaborative mentor and team player who raises standards through clear feedback and support.