Data Science Engineer (Apprentice)

DraftKings
City of London, England
6 months ago
Applications closed

Related Jobs

View all jobs

Protection Scientist Engineer, Intelligence and Investigations

OpenAI London, United Kingdom
Permanent

Data Director - Logistics & Fulfilment

Ocado United Kingdom

Machine Learning Software Engineer, Research

PhysicsX London, United Kingdom
£40,000 – £80,000 pa On-site

Staff Machine Learning Software Engineer, Research

PhysicsX London, United Kingdom
£70,000 – £120,000 pa On-site

AI Product Manager

167 Solutions London, United Kingdom
£130,000 – £150,000 pa Hybrid

Data Engineer

Harnham - Data & Analytics Recruitment London, United Kingdom
£70,000 – £90,000 pa Hybrid
Posted
20 Oct 2025 (6 months ago)

At DraftKings, AI is becoming an integral part of both our present and future, powering how work gets done today, guiding smarter decisions, and sparking bold ideas. It\'s transforming how we enhance customer experiences, streamline operations, and unlock new possibilities. Our teams are energized by innovation and readily embrace emerging technology. We\'re not waiting for the future to arrive. We\'re shaping it, one bold step at a time. To those who see AI as a driver of progress, come build the future together.

The Crown Is Yours

As an Associate Data Science Engineer, you\'ll join a team that blends sports modelling expertise with machine learning to power our Sportsbook platform. You\'ll design, test, and deploy models that deliver real business impact—bringing together your creativity, statistical skills, and engineering mindset. This role is part of the UK Apprenticeship Programme in partnership with Northeastern University, offering a fully funded Master\'s Degree in Data Science. You\'ll spend 80% of your time working on live projects at DraftKings and 20% on advancing your academic learning.

What You\'ll Do
  • Create and test statistical and machine learning models to predict sporting outcomes.
  • Build and manage sportsbook data assets to support the development of data science models.
  • Establish and monitor reliable data flows between data science applications and the wider organisation.
  • Implement data science applications in Python.
  • Create automated tests to ensure the accuracy and reliability of models and applications.
  • Design advanced data-driven tools for monitoring and analytics.
  • Explore and experiment with new approaches to optimise model performance and improve data science workflows.
  • Utilize AI and machine learning techniques to enhance model accuracy, automate processes, and uncover innovative solutions to sports modelling challenges.
What You\'ll Bring
  • Bachelor\'s degree in Statistics, Data Science, Mathematics, Computer Science, Engineering, or related field is required for this program.
  • Experience using Python and its application to data science and data engineering.
  • Knowledge of object-oriented programming is beneficial.
  • Some understanding of data science and statistical modelling principles will be considered an asset.
  • As this program is partially funded by the Government, we can only accept applications from candidates who are based in England and enrolled in Level 7 (Masters) programmes.
Join Our Team

We\'re a publicly traded (NASDAQ: DKNG) technology company headquartered in Boston. As a regulated gaming company, you may be required to obtain a gaming license issued by the appropriate state agency as a condition of employment. Don\'t worry, we\'ll guide you through the process if this is relevant to your role.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

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

Industry Insights

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

Where to Advertise Machine Learning Jobs in the UK (2026 Guide)

Advertising machine learning jobs in the UK requires a different approach to most technical hiring. The candidate pool is small, highly specialised and in demand across AI labs, financial services, healthcare, autonomous systems and consumer technology simultaneously. Machine learning engineers and researchers move between roles through professional networks, conference communities and specialist platforms — not general job boards where ML roles compete with unrelated software engineering positions for the same audience. This guide, published by MachineLearningJobs.co.uk, covers where to advertise machine learning roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.