Data Scientist

About Faculty

Faculty transforms organisational performance through safe, impactful and human-led AI.

We are Europe’s leading applied AI company, and saw its potential a decade ago - long before the current hype cycle.

We founded in 2014 with our Fellowship programme, training academics to become commercial data scientists.

Today, we provide over 300 global customers with industry-leading software, and bespoke AI consultancy for retail, healthcare, energy, and governmental organisations, as well as our award winning Fellowship.

Our expertise and safety credentials are such that OpenAI asked us to be their first technical partner, helping customers deploy cutting-edge generative AI safely.

Our high-impact work has saved lives through forecasting NHS demand during covid, produced green energy by routing boats towards the wind, slashed marketing spend by predicting customer spending habits, and kept children safe online.

AI is an epoch-defining technology. We want people to join us who can help our customers reap its enormous benefits safely.

What you'll be doing:

As a data scientist in our Consumer and Services vertical, fundamentally your role is to help customers solve their problems using data science and AI; this involves applying a variety of techniques, ranging from simple data analysis to designing and implementing bespoke machine learning algorithms. Previous work has included using causal modelling to target promotional campaigns and more recently utilising LLMs to create customer service assistant - more of our case studies can be found here.

Prior knowledge of these techniques is not a prerequisite as we are looking for both experienced candidates and those who want to learn. We anticipate that with our support, you could become an expert in one of these areas even if you don’t yet have much hands-on experience.

Additionally, your contribution won't be limited to your technical skills. Using practical and business sense, you will help our excellent commercial team build lasting relationships with our customers, shaping the direction of both current and future projects.

As we are a growing business, we need people who take initiative. You will take on more responsibility from day one than you would expect in comparable roles elsewhere, whilst in turn benefitting from the support and mentorship of our more seasoned team members.

Data scientists at Faculty take pride in:

• Solving problems with the best data-science techniques and the scientific method
• Communicating technical content at the right level both internally and to customers.
• Fostering a collaborative work environment, sharing knowledge, and bringing the best out of everyone in the team.
• Seeking out innovative ways to help Faculty grow, for example, by developing shared technical and non-technical resources.

Who we are looking for:

• Experience from quantitative academic research (e.g. STEM PhD), professional data-science positions, or a combination of the two.
• Python programming experience as evidenced by earlier work in data science, academic research or software engineering.
• The ability to reason mathematically and an understanding of common statistical tests and/or probability.
• Experience using common machine learning algorithms as evidenced by previous work or side projects, with the ability to think creatively when an innovative solution is necessary.
• Experience of manipulating data using the standard libraries for data science (e.g. NumPy, Pandas, Scikit-Learn or equivalents in other programming languages).
• An appreciation for the scientific method as applied to the commercial world; the ability to turn client requests into problems that can be solved using data science; resourcefulness in overcoming difficulties through creativity, commitment and collaboration; and an inquisitive and questioning mindset in evaluating the performance and impact of models upon deployment.
• An interest in working alongside our customers and to learn about the commercial aspects of the job.
• Effective verbal and written communication - you should be comfortable with presenting your work in front of customers.
• The ability to follow a project plan and stick to deadlines, as well as proactively solve problems that emerge.

The following would be a bonus, but are by no means required:

• Prior commercial experience, particularly if this involved customer-facing work or project management.
• Research experience (PhD or Postdoc) as evidenced by academic publications and conference talks.
• Experience working with generative AI models e.g LLMs.
• Working knowledge of any of following ML domains: NLP, Bayesian inference, computer vision, deep learning, causal modelling, AI safety
• Experience with Docker and containerisation
• Experience creating web apps using e.g. FastAPI, Flask, React.js
• Familiarity with MLOps including deployment, monitoring and scalability tooling.