Data Scientist - Fixed Term Contract

Faculty
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist - New

Data Scientist / Software Engineer

Data Scientist - Contract - 12 months

Data Scientist (Globally Renowned Retail Group)

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.


This will be a fixed‑term contract that should run until March 29th, 2026.


Why now?

We're in a period of significant growth, expanding our capacity to serve a diverse range of customers and apply AI to real‑world problems with tangible impact. To meet this demand and maintain our high standards of delivery, we need skilled Data Scientists to join us for a temporary period and own end‑to‑end project delivery, build strong customer relationships, and help us scale our business.


What you'll be doing:

As a Data Scientist in our Applied AI team unit you will be part of project teams that deliver bespoke algorithms to our clients across a range of different sectors. You will be responsible for conceiving the data science approach, for designing the associated software architecture, and for ensuring that best practices are followed throughout.


You will help our excellent commercial team build strong relationships with clients, shaping the direction of both current and future projects. Particularly in the initial stages of commercial engagements, you will guide the process of defining the scope of projects to come with an emphasis on technical feasibility. We consider this work as fundamental towards ensuring that Faculty can continue to deliver high‑quality software within the allocated timeframes.


Thanks to Faculty platform, you will have access to powerful computational resources, and you will enjoy the comforts of fast configuration, secure collaboration and easy deployment. Because your work in data science will inform the development of our AI products, you will often collaborate with software engineers and designers from our dedicated product team.


Who we're looking for:

  • Proven experience in either a professional data science position or a quantitative academic field


  • Strong programming skills as evidenced by earlier work in data science or software engineering. Although your programming language of choice (e.g. R, MATLAB or C) is not important, we do require the ability to become a fluent Python programmer in a short timeframe


  • An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit‑Learn) and familiarity with a deep‑learning framework (e.g. TensorFlow, PyTorch, Caffe)


  • A high level of mathematical competence and proficiency in statistics


  • A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model cross validation, Bayesian inference, time‑series analysis, simple NLP, effective SQL database querying, or using/writing simple APIs for models. We regard the ability to develop new algorithms when an innovative solution is needed as a fundamental skill


  • An appreciation for the scientific method as applied to the commercial world; a talent for converting business problems into a mathematical framework; resourcefulness in overcoming difficulties through creativity and commitment; a rigorous mindset in evaluating the performance and impact of models upon deployment


  • Some commercial experience, particularly if this involved client‑facing work or project management; eagerness to work alongside our clients; business awareness and an ability to gauge the commercial value of projects; outstanding written and verbal communication skills; persuasiveness when presenting to a large or important audience


  • Experience leading a team of data scientists (to deliver innovative work according to a strict timeline) as well as experience in composing a project plan, in assessing its technical feasibility, and in estimating the time to delivery



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.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.