Data Scientist

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.

The role

As a Data Scientist in Faculty’s Energy Transition & Environment team, you’ll apply cutting-edge AI to some of the most urgent challenges in the low-carbon transition. From helping National Grid manage a renewable-powered electricity system, to forecasting network flexibility for Northern Powergrid, reducing emissions from SGN’s gas network, and optimising routes for Drift’s energy-generating boats - our projects create measurable real-world impact. This is a hands-on, fast-paced role where you’ll turn complex problems into deployable data science solutions, working side-by-side with customers and multidisciplinary teams to deliver outcomes that matter.

What You’ll Be Doing

• Designing, developing, and deploying data science solutions - from exploration to production.
• Applying advanced statistical, analytical, and machine learning techniques, including Bayesian modelling and time-series forecasting.
• Translating business problems into solvable data challenges, shaping delivery approaches that maximise value.
• Working closely with engineers, designers, and product managers to ensure solutions are scalable, maintainable, and impactful.
• Communicating technical concepts clearly to both technical and non-technical audiences.
• Contributing to Faculty’s data science community by sharing knowledge, refining best practices, and supporting colleagues.

What We’re Looking For

You’ll have strong foundations in data science, a creative problem-solving mindset, and curiosity about the energy sector.

• Experience from quantitative academic research (e.g. STEM PhD) or professional data science roles.
• Programming experience - ideally in Python or transferable from other languages (e.g. R, MATLAB, C).
• Solid mathematical reasoning and knowledge of statistics and probability.
• Experience with machine learning algorithms and data manipulation libraries (e.g. NumPy, Pandas, Scikit-Learn).
• Strong communication skills and ability to present work confidently to stakeholders.
• Ability to follow a plan, meet deadlines, and adapt when challenges arise.

Bonus Skills

• Prior commercial or customer-facing experience.
• Knowledge in NLP, Bayesian inference, computer vision, deep learning, or causal modelling.
• Experience building web apps or working with MLOps tooling.

Why join Faculty?

You’ll work alongside exceptional colleagues from diverse backgrounds, united by a passion for solving hard problems with AI. We offer:

• Complex, high-impact projects in the low-carbon transition.
• A culture of continuous learning, mentorship, and rapid professional growth.
• The opportunity to shape a high-growth business and make a tangible difference.

At Faculty, you’ll develop faster than you thought possible - and see your work make a real-world impact.

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.

Seniority Level

Entry level

Employment Type

Full-time

Job Function

Engineering and Information Technology

Industries

Technology, Information and Internet

Location: London, England, United Kingdom

You’ll have strong foundations in data science, a creative problem-solving mindset, and curiosity about the energy sector.

• Experience from quantitative academic research (e.g. STEM PhD) or professional data science roles.
• Programming experience - ideally in Python or transferable from other languages (e.g. R, MATLAB, C).
• Solid mathematical reasoning and knowledge of statistics and probability.
• Experience with machine learning algorithms and data manipulation libraries (e.g. NumPy, Pandas, Scikit-Learn).
• Strong communication skills and ability to present work confidently to stakeholders.
• Ability to follow a plan, meet deadlines, and adapt when challenges arise., * Prior commercial or customer-facing experience.
• Knowledge in NLP, Bayesian inference, computer vision, deep learning, or causal modelling.
• Experience building web apps or working with MLOps tooling.

You’ll work alongside exceptional colleagues from diverse backgrounds, united by a passion for solving hard problems with AI. We offer:

• Complex, high-impact projects in the low-carbon transition.
• A culture of continuous learning, mentorship, and rapid professional growth.
• The opportunity to shape a high-growth business and make a tangible difference.

At Faculty, you’ll develop faster than you thought possible - and see your work make a real-world impact.

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.