at Faculty
Full TimeFaculty 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.
We operate a hybrid way of working, meaning that you'll split your time across client location, Faculty's Old Street office and working from home depending on the needs of the project. For this role, you may be required to be client-side for up-to three days per week at times and working either from home or our Old street office for the rest of your time.
As a data scientist in our Defence BU, 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.
Faculty has a growing footprint in Defence, where we solve hard data science problems for the Ministry of Defence and other clients operating in NATO countries. We have taken learnings from work in other sectors and now apply them to defence. Examples of our work in other sectors include: using Bayesian hierarchical modelling to develop an early warning system for the NHS during the COVID-19 pandemic, modelling 3D point cloud data to identify and measure assets for Network Rail, and using NLP to identify topics in market research.
We are looking for candidates with deep knowledge of analysis of sonar signals and who are interested in exploring how cutting edge ML techniques can be applied. Prior deep knowledge of AI/ML 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 and have a passion for solving complex problems.
Your role will evolve alongside business needs, but you can expect your key responsibilities to include:
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.
Proficiency in programming languages commonly used in sonar applications, such as C/C++, Python, and MATLAB and experience with software development tools and environments, such as Visual Studio, Eclipse, and version control systems like Git.
In-depth knowledge of sonar principles, including active and passive sonar, side-scan sonar, and synthetic aperture sonar.
Proficiency in using sonar simulation and modeling tools (e.g., MATLAB, LabVIEW) and experience with sonar hardware and software, including embedded systems and real-time processing.
Practical exposure to designing, developing, testing, and troubleshooting sonar systems.
Knowledge of specific sonar applications, such as underwater navigation, marine biology, defence, and underwater archaeology.
The ability to reason mathematically and an understanding of common statistical tests and/or probability.
An interest in working alongside our customers and to learn about the commercial aspects of the job.
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.
Working knowledge of any of following ML domains: NLP, Bayesian inference, computer vision, deep learning, causal modelling, AI safety
Working knowledge of the standard libraries for data science (e.g. NumPy, Pandas, Scikit-Learn or equivalents in other programming languages).
Experience creating web apps using e.g. Dash, Flask, React.js
Familiarity with MLOps including deployment, monitoring and scalability tooling.
