Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Science Engineer (Apprentice)

DraftKings
City of London
2 weeks ago
Create job alert

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

Related Jobs

View all jobs

Data Science Engineer

Lead R Engineer / Data Scientist - Integrated Pest Management & Soil Science

Data Science Engineer (Apprentice)

Data Science Engineer- eDV Clearance

Lead R Engineer / Data Scientist - Integrated Pest Management & Soil Science

Senior Data Science Engineer, American Football

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.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.