Data Science Manager

Kindred Group

London, United Kingdom

We are looking for a Data Science Manager who will lead a team of Data Scientists responsible for our Customer Risk workstream. You will report to the Senior Data Science Manager. You will be accountable for execution against our roadmaps, aligning closely with our machine learning engineering team as development transitions between research and engineering phases.

What you will do

Take ownership of execution against our Risk research roadmap by overseeing tech prioritisation and balancing hands-on contributions with managing and coaching a team of Data Scientists;
Take joint accountability with ML Engineering for the lifecycle of ML data products as they transition from research to production;
Line management, coaching, and professional development of an existing team of Data Scientists;
Recruitment of Data Scientists, ensuring alignment with team's evolving skill requirements;
Identification of skill and capability gaps and proactively addressing them;
Keeping your team engaged, motivated, and supported;
Maintain strong relationships and communication at the interfaces of our domain, namely with our business owners and engineering teams;
Contribute to building a culture of excellence within the wider Data Science, Quant, and MLE organisation by aligning tools, best practice, process, and governance;
Raise the profile of the team by actively promoting work and team contributions across the organization.

Your experience

Bachelor's or advanced degree in a STEM subject;
Experience managing a team of Data Scientists developing machine learning data products. Ability to engage, motivate, and empathise with individuals;
In-depth understanding and experience building risk algorithms and products;
Advanced knowledge of machine learning algorithms, general statistical methodologies and theory;
Advanced knowledge of AB testing and design of experiment;
Exemplary Python programming and SQL skills, experience using Spark for processing large datasets;
Familiarity with cloud computing platforms, preferably experience with Amazon Web Services;
Understanding of software product development processes and governance, including CI/CD processes, release and change management;
Understanding of machine learning product lifecycle, and how scientists and engineers collaborate in cross-functional product teams;
Understanding of ML deployment paradigms including batch, event driven, and request-response;
Knowledge of gitops processes for testing and deployment, e.g. Jenkins and Terraform. Broad awareness of software development practice;
Passionate about the personal development of self and team members;
Interest and understanding of sports betting, experience in sports risk management;
Excellent interpersonal skills, able to explain complex concepts to stakeholders;
A problem-solving growth mindset with the ability to pick up new concepts quickly.