Data Science Manager

Kindred Group plc
London
1 week ago
Create job alert

#LI-LB1

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 Personalisation workstream. You will report to the Senior Data Science Manager responsible for workstreams supporting our Product business vertical. 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.

Key responsibilities:

  1. Take ownership of execution against our Personalisation research roadmap by overseeing tech prioritisation and balancing hands-on contributions with managing a team of Data Scientists and providing technical guidance.
  2. Take joint accountability with ML Engineering for the lifecycle of ML data products as they transition from research to production.
  3. Line management, coaching, and professional development of an existing team of 3 Data Scientists.
  4. Recruitment of Data Scientists, ensuring alignment with team's evolving skill requirements.
  5. Identification of skill, capability gaps and proactively addressing them, ensuring continuous skill development.
  6. Keeping your team engaged, motivated, and supported.
  7. Maintain strong relationships and communication at the interfaces of our domain, namely with our business owners and engineering teams.
  8. Contribute to building a culture of excellence within the wider Data Science, Quant, and MLE organisation by aligning tools, best practice, process, and governance.
  9. Raise the profile of the team by actively promoting work and team contributions across the organization.

Your experience

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

#J-18808-Ljbffr

Related Jobs

View all jobs

Applied Science Manager, Traffic Quality ML

Lead Data Scientist - Pricing

Senior Data Scientist, Team Lead

Data & Analytics Manager

Data Science Consultant

Data Scientist

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Leadership for Managers: Strategies to Motivate, Mentor, and Set Realistic Goals in Data-Driven Teams

Machine learning (ML) has become an indispensable force in the modern business world, influencing everything from targeted marketing campaigns to advanced medical diagnostics. As industries integrate predictive algorithms and data-driven decision-making into their core operations, the need for effective leadership in machine learning environments has never been greater. Whether you’re overseeing a small team of data scientists or spearheading an enterprise-scale ML project, your leadership style must accommodate rapid innovation, complex problem-solving, and diverse stakeholder expectations. This guide provides actionable insights into how you can motivate, mentor, and establish achievable goals for your machine learning teams—ensuring they thrive in data-driven environments.

Top 10 Books to Advance Your Machine Learning Career in the UK

Machine learning (ML) remains one of the fastest-growing fields within technology, reshaping industries across the UK from finance and healthcare to e-commerce, telecommunications, and beyond. With increasing demand for ML specialists, job seekers who continually update their knowledge and skills hold a significant advantage. In this article, we've curated ten essential books every machine learning professional or aspiring ML engineer in the UK should read. Covering foundational theory, practical implementations, advanced techniques, and industry trends, these resources will equip you to excel in your machine learning career.

Navigating Machine Learning Career Fairs Like a Pro: Preparing Your Pitch, Questions to Ask, and Follow-Up Strategies to Stand Out

Machine learning (ML) has swiftly become one of the most in-demand skill areas across industries, with companies leveraging predictive models and data-driven insights to solve challenges in healthcare, finance, retail, manufacturing, and beyond. Whether you’re an early-career data scientist aiming to break into ML, a seasoned engineer branching into deep learning, or a product manager exploring AI-driven solutions, machine learning career fairs offer a powerful route to connect with prospective employers face-to-face. Attending these events can help you: Network with hiring managers and technical leads who make direct recruitment decisions. Gain insider insights on the latest ML trends and tools. Learn about emerging job roles and new industry verticals adopting machine learning. Showcase your interpersonal and communication skills, both of which are increasingly important in collaborative AI/ML environments. However, with many applicants vying for attention in a bustling hall, standing out isn’t always easy. In this detailed guide, we’ll walk you through how to prepare meticulously, pitch yourself confidently, ask relevant questions, and follow up effectively to land the machine learning opportunity that aligns with your ambitions.