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Faculty

Company

Data Scientist

London - Hybrid

Job Description

About Faculty


At Faculty, we transform organisational performance through safe, impactful and human-centric AI.

With more than a decade of experience, we provide over 350 global customers with software, bespoke AI consultancy, and Fellows from our award winning Fellowship programme.

Our expert team brings together leaders from across government, academia and global tech giants to solve the biggest challenges in applied AI.

Should you join us, you’ll have the chance to work with, and learn from, some of the brilliant minds who are bringing Frontier AI to the frontlines of the world.

About the Team

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 believe that the responsible, and intelligent, deployment of AI is critical to the success of this mission. 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.

About the role:

As a Data Scientist, you will work closely with clients and cross functional teams to define project scope, ensure technical feasibility, and drive delivery excellence.


You’ll design and deliver bespoke data science solutions, shaping the technical direction of high-impact projects and solidifying our reputation as a leader in practical, measurable AI.

What you'll be doing:

  • Mapping the end-to-end data science approach and designing the associated software architecture for projects

  • Driving the technical scoping and feasibility assessment of new projects

  • Building strong client relationships by acting as a technical advisor and shaping the direction of current and future engagements

  • Delivering bespoke algorithms and scalable software solutions that adhere to best practices for high-stakes decision-making

  • Setting the technical bar for the project team, ensuring the highest standards of code, rigour, and delivery quality (IC leadership)

  • Contributing to Faculty's thought leadership and reputation through teaching, public speaking, or open-source projects

Who we're looking for:

  • You have proven experience in a professional data science or quantitative academic role, underpinned by high mathematical and statistical competence.

  • You are a strong Python programmer, proficient in essential libraries (NumPy, Pandas) and a deep-learning framework (TensorFlow/PyTorch).

  • You possess a solid grasp of core data science techniques (supervised/unsupervised learning, time-series, NLP, model validation) and the ability to innovate new algorithms.

  • You apply a rigorous scientific and entrepreneurial mindset, translating complex business problems into a mathematical framework and measuring model impact upon deployment.

  • You are an exceptional communicator, adept at translating complex technical solutions into persuasive, actionable insights for senior and non-technical audiences.

  • You contribute to team success by project planning, assessing technical feasibility, estimating delivery timelines, and achieving measurable outcomes.

What we can offer you:

The Faculty team is diverse and distinctive, and we all come from different personal, professional and organisational backgrounds. We all have one thing in common: we are driven by a deep intellectual curiosity that powers us forward each day.

Faculty is the professional challenge of a lifetime. You’ll be surrounded by an impressive group of brilliant minds working to achieve our collective goals.

Our consultants, product developers, business development specialists, operations professionals and more all bring something unique to Faculty, and you’ll learn something new from everyone you meet.

Please mention that you found this job on MoAIJobs, this helps us grow. Thank you!

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About the job

Posted on

Nov 13, 2025

Apply before

Dec 13, 2025

Job typeFull-time
CategoryData Science

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