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    Senior Data Scientist - Bristol, United Kingdom - Faculty

    Faculty
    Faculty Bristol, United Kingdom

    1 week ago

    Default job background
    Full time
    Description

    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 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.

    What you'll be doing:

    As a Senior Data Scientist in our Defence business unit you will lead project teams that deliver bespoke algorithms to our clients across the defence and national security sector. 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 directly contribute to the code base of the project with a specific focus on the components that are difficult or require experience to implement. Examples of the type of project you might work on include using Bayesian hierarchical modelling to develop an , modelling 3D point cloud data to , and using NLP to .

    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.

    You will play an important role in the development of others at Faculty by acting as the designated mentor of a small number of data scientists, and by supporting the professional growth of data scientists on the project team. The latter includes giving targeted support where needed, and providing step-up opportunities where helpful.

    Faculty has earned wide recognition as a leader in practical data science. You will actively contribute to the growth of this reputation by delivering courses to high-value clients, by talking at major conferences, by participating in external roundtables, or by contributing to large-scale open-source projects. You will also have the opportunity to teach on the fellowship about topics that range from basic statistics to reinforcement learning, and to mentor the fellows through their 6-week project.

    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:

  • Mid to Senior 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
  • A leadership mindset focussed on growing the technical capabilities of the team; a caring attitude towards the personal and professional development of other data scientists; enthusiasm for nurturing a collaborative and dynamic culture
  • 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
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