Data Science & AI Manager

Аналитика рынка

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Описание

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 an

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