Data Scientist, Marketing Effectiveness - Bristol, United Kingdom - Faculty

Description

Faculty transforms organisational performance through safe, impactful and human-led AI.
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 can expect to be client-side for up-to three days per week and working from home for the majority of 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.

We have previously worked on a multitude of different technical solutions for our clients, 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 .

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.

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.
Seeking out innovative ways to help Faculty grow, for example, by developing shared technical and non-technical resources.
Experience from quantitative academic research (e.g. STEM PhD), professional data-science positions, or a combination of the two.
Programming experience as evidenced by earlier work in data science, academic research 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.

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; 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.

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.
NLP, Bayesian inference, computer vision, deep learning, causal modelling, AI safety
Experience creating web apps using e.g. Dash, Flask,
Familiarity with MLOps including deployment, monitoring and scalability tooling.
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