About Kindred
Kindred Group is a digital entertainment pioneer bringing together nine successful online gambling brands, forming one of the largest online gambling groups in the world. Our purpose is to transform gambling by being a trusted source of entertainment that contributes positively to society. Our goal is that 0% revenue is derived from harmful gambling.
Our global team of more than 2000 people represents 70+ nationalities. When you join Kindred, you’ll be part of a collaborative, diverse and inclusive team that has your best interest at heart. We are a trusting company that knows the value of a healthy work-life balance. We offer a wide range of benefits, along with annual bonus, which is tied to both company and your individual performance.

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The role
We are building the sportsbook of tomorrow at Kindred. Machine learning algorithms and AI are at the heart of how we will deliver an unrivalled customer experience and unlock operational efficiencies through automation.
Our Data Science (DS) and Machine Learning Engineering (MLE) teams are currently focused on delivering Personalisation and Risk Management products, with future scope to develop sportsbook specific Reward products, and collaborate with our Quant team to develop trading automation solutions.
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.

Our Way Of Working
Our world is hybrid.
A career is not a sprint. It’s a marathon. One of the perks of joining us is that we value you as a person first. Our hybrid world allows you to focus on your goals and responsibilities and lets you self-organise to improve your deliveries and get the work done in your own way.
Application Process
Click on the “” button and complete the short web form. Please add your CV and covering letter in English to let us know your motivation for applying and your salary expectation. Our Talent Acquisition team will be in touch soon. Kindred is an equal opportunities employer committed to employing a diverse workforce and an inclusive culture. As such we oppose all forms of discrimination in the workplace. We create equal opportunities for all our applicants and will treat people equally regardless of and not limited to, gender, ages, disability, race, sexual orientation. We are committed not only to our legal obligations but also to the positive promotion that equal opportunities bring to our operations as set out in our sustainability framework. Kindred has an ESG rating of AAA by MCSI.

• 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