Data Scientist
London - Hybrid
Faculty
Faculty partners with organisations to enhance performance through cutting-edge artificial intelligence solutions, driving real-world impact.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.
About the role
As a Data Scientist within Frontier, you will be part of the cross-functional delivery teams that configure our product Frontier for our customers. Each deployment of Frontier requires a computational twin, essentially an AI-powered digital twin, to be built, and it is primarily the responsibility of our data scientists to both design and then build these computational twins.
Whilst doing this well is partly about building familiarity with Frontier’s development interfaces, it’s mainly about doing things that are critical in any applied data science role; namely deeply understanding the core customer problem in order to ensure that the technical solution can drive value. In terms of more technical activities, the build of a computational twin involves a bunch of tasks common to all data science work such as EDA, model building, and model evaluation.
Who we're looking for:
Experience in either a professional data science position or a quantitative academic field
Strong python programming skills as evidenced by earlier work in data science or software engineering.
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 customers; 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
A product mindset, able to understand the needs of users and to learn how Frontier delivers them value
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.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: APIs Bayesian Caffe EDA Engineering Machine Learning NLP NumPy Pandas Python PyTorch Scikit-learn SQL Statistics TensorFlow
Perks/benefits: Team events
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