About
We established Faculty in 2014 because we thought that AI would be the most important technology of our time. Since then, we've worked with over 350 global customers to transform their performance through human-centric AI. We don't chase hype cycles. We innovate, build and deploy responsible AI which moves the needle. We bring an unparalleled depth of technical, product and delivery expertise to our clients who span government, finance, retail, energy, life sciences and defence.
Our Energy, Transition and Environment business unit is pioneering meaningful change in the clean energy revolution. Our vision is to accelerate the transition to net-zero emissions and drive efficiencies for a new era of utility companies. We partner with a wide range of clients - from major energy operators, to GreenTech startups, and national infrastructure providers - to build solutions which return measurable impact and move us towards a smarter, cleaner, and more sustainable world.
As a Lead Data Scientist, you'll take on a pivotal, entrepreneurial role, functioning as the technical expert who thrives on complexity and commercial impact. You will be responsible for setting the technical direction and ensuring the high-quality, scalable delivery of our most challenging, high-impact projects. This position combines deep expertise in machine learning with strategic oversight to define project roadmaps, manage technical risk, and architect reliable solutions. Your focus will be on driving innovation, mentoring cross-functional teams, and actively shaping both our technical standards and long-term customer relationships.
Responsibilities
•Setting the technical direction for complex, business-critical projects and expertly balancing trade-offs between speed, innovation, and reliability.
•Designing and implementing reliable, production-grade technical solutions, ensuring comprehensive documentation of architectures and specifications.
•Defining project problems, developing clear roadmaps, and overseeing end-to-end delivery across multi-disciplinary workstreams.
•Leading technical scoping and feasibility studies for high-value sales opportunities and strategic customer engagements.
•Managing relationships and communications with demanding clients, fostering trust and aligning technical solutions with shared long-term commercial goals.
•Driving the adoption of best practices, shared resources, and robust technical processes across the wider Data Science craft.
•Mentoring and developing other data scientists and team members, actively contributing to the growth and technical excellence of the organisation.
Who we're looking for
•You bring depth of expertise in at least one machine learning domain and strong technical breadth across the entire data science landscape.
•You are a skilled technical leader, proficient in mentoring individuals, managing teams (including other managers), and rolling out impactful tools and workflows.
•You have proven project management expertise, capable of dividing complex, ill-defined problems into actionable, clearly defined workstreams with timelines you can defend.
•You are adept at managing ill-defined, high-risk tasks, consistently delivering innovative and practical outcomes under commercial pressure.
•You possess strong customer leadership skills, able to act as a trusted technical advisor and drive long-term strategic relationships with demanding clients.
•You excel at cross-functional collaboration, effectively aligning technical strategy with Engineering, Commercial (BD), and Infrastructure teams.
•You have experience extending technical oversight to business unit-level initiatives, using your vision to influence and contribute to organisational success.
Our Interview Process
•Talent Team Screen (30 minutes)
•Introduction to the team (45 minutes)
•Technical Interview (90 minutes)
•Commercial Interview (60 minutes)
Notes
We are open to conversations about part-time hours. We're happy for you to use AI for research and interview prep, but please don't use it to generate answers during live interviews. We also use an AI note-taker (Metaview) in interviews so interviewers can stay present (you can opt out), and every application is reviewed by a human, never decided by AI.
