Data Science

Join iLOF to manage the day-to-day delivery of the Data Science team, working with physicists, biologists and engineers to turn optical measurements into validated models for blood-based diagnostics. You will work with iLOF’s scientific and executive leadership, take the company's priorities and turn them into clear plans, and make sure the team delivers rigorous, well-engineered work on time. You will also own how the team learns and grows, and lead iLOF's strategy for adopting AI tools in Data Science work. This is a hands‑on management role for someone who has led technical teams in industry. You do not need to be a physicist, but you will be comfortable with signal data and able to speak the language of the teams you work with.

Job Description
Manage the Data Science team day-to-day: set clear expectations, run 1:1s, give regular feedback, support development plans and handle performance. Turn priorities set by scientific and executive leadership into sprint and project plans; allocate people, manage dependencies and track milestones. Be the first point of call for prioritisation questions, escalating trade-offs early and keeping the team focused on what matters most. Keep projects on track from scoping through validation and hand-over, flagging risks and slippage early. Make sure software is built properly: version control, code review, testing, reproducible pipelines and documentation, working closely with the software team. Bridge the gap between development and production-ready requirements, so prototypes and models move into robust, validated and deployable products. Bring structure to how the team works: lightweight processes, clear definitions of done, and standards for analysis review and model validation. Work across Biology, Photonics, Engineering and Clinical teams so data acquisition, analysis and product needs stay aligned. Train and upskill the team: identify skill gaps, run structured training, pair programming and code/analysis reviews, and bring in external courses or experts where needed, so the team keeps pace with modern data science, ML and software practice. Lead AI tools strategy and implementation: set the strategy for how the team uses AI tools (coding assistants, LLMs and agents, AutoML and experiment tooling), select and roll out tools, define guidelines for safe and rigorous use, including data protection and validation of AI-generated work, measure the impact on productivity and quality, and share what works with the wider company. Report progress, risks and decisions clearly to leadership. Contribute to hiring and onboarding into the team. Qualifications Who we are looking for You have led technical teams in industry and know how to get a group of strong specialists to deliver. You are organised, calm under changing priorities and comfortable saying what is and isn't a priority. You understand data science and machine learning well enough to judge quality and spot problems, even if you are no longer the deepest technical expert in the room. You enjoy teaching, and you are an early, critical adopter of AI tools who can bring a whole team along. You are used to running several projects in parallel, each with its own timeline and owner, and to keeping those owners aligned on what the team can deliver and when. Qualifications Must have Several years of industry experience leading data science, machine learning or engineering teams to delivery (managing people, not only projects). Degree (MSc or PhD preferred) in a quantitative field such as electrical engineering, signal processing, physics, applied mathematics, statistics or computer science. Hands‑on background in data science or ML, with a working understanding of modelling, validation and the pitfalls of small or noisy datasets. Strong software engineering practice: you can set and enforce standards for code quality, testing and reproducibility. Hands‑on experience leading the adoption of AI tools in a technical team: choosing tools, rolling them out, setting usage guidelines and showing measurable gains. A track record of developing people: designing training, coaching and mentoring that visibly raised a team's skills. Proven project and delivery management: planning, prioritisation, risk management and stakeholder reporting. Experience running Agile delivery (for example Scrum or Kanban): sprint planning, backlog management, stand‑ups and retrospectives. Hands‑on proficiency in Python, SQL and Git, and practical experience building with agentic AI (LLM agents, tool use and multi‑step automated workflows). Excellent communication in English with technical and non‑technical colleagues. Experience with signal or sensor data (signal processing, spectroscopy, time series, instrumentation). Experience in a scale‑up or start‑up with an AI or deep‑tech component Work experience on clinically certified or other regulated products (for example CE-IVD or FDA-cleared devices, or software as a medical d