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- Tasks: Create and implement machine learning models for sports data applications.
- Company: Join DraftKings, a leading tech company revolutionising sports and entertainment experiences.
- Benefits: Enjoy flexible work options, competitive pay, and opportunities for personal growth.
- Why this job: Tackle exciting challenges while shaping the future of responsible gaming in a dynamic team.
- Qualifications: Proficiency in Python and experience with statistical modelling; a passion for sports is essential.
- Other info: Opportunity to coach junior data scientists and collaborate across teams.
The predicted salary is between 43200 - 72000 ÂŁ per year.
We’re defining what it means to build and deliver the most extraordinary sports and entertainment experiences. Our global team is trailblazing new markets, developing cutting-edge products, and shaping the future of responsible gaming. Here, “impossible” isn’t part of our vocabulary. You’ll face some of the toughest but most rewarding challenges of your career. They’re worth it. Channeling your inner grit will accelerate your growth, help us win as a team, and create unforgettable moments for our customers.
Our team comprises sports modelling experts and data science technologists, coming together to develop innovative DS products that deliver incremental value on the Sportsbook platform at DraftKings. As part of this role, you will be a creative thinker, utilizing data, machine learning, and software development skills to craft high-impact best-in-class sports models that grow the business.
What you’ll do as a Senior Data Science Engineer:
- Create statistical and machine learning models and integrate them into DS applications
- Collect and engineer sports data assets to assist in model development
- Implement the sports models and pricing engines in Python
- Create automatic tests to ensure model and pricing engine accuracy
- Collaborate closely with product, developers, QAs and delivery leads to move projects from ideation to deployment
- Test that data flows work as expected and that models are well integrated in larger business context
- Research the different sport’s specifications and rules
- Coach and support more junior data scientists within the team
What you’ll bring:
- Highly proficient in at least one programming language, ideally Python
- Experience of building statistical or machine learning models for multiple sports
- Understanding of data science and statistical modelling principles
- Experience with Kubernetes and Kafka are desirable
- Knowledge of MLOps principles and related tools will be considered an asset
- Self-learner who is open to learning new things
- Familiarity with version control concepts
- Understanding of object-oriented programming principles
- Understanding of Sportsbook products will be considered an asset
- Keen interest in sports
- PhD, Masters or Bachelor’s degree in Statistics, Data Science, Mathematics, Computer Science, Engineering, or related field
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.
Contact Detail:
DraftKings Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Science Engineer - Tennis
✨Tip Number 1
Familiarise yourself with the latest trends in sports data science, especially related to tennis. Understanding how data models are applied in this specific sport will give you an edge during interviews and discussions.
✨Tip Number 2
Engage with online communities or forums focused on sports analytics and data science. Networking with professionals in the field can provide insights into the role and may even lead to referrals.
✨Tip Number 3
Brush up on your Python skills, particularly in relation to machine learning libraries like TensorFlow or scikit-learn. Being able to demonstrate your coding proficiency in practical scenarios can set you apart from other candidates.
✨Tip Number 4
Prepare to discuss your experience with MLOps and any relevant tools you've used. Highlighting your understanding of deploying models in a production environment will show that you're ready to contribute from day one.
We think you need these skills to ace Senior Data Science Engineer - Tennis
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data science, particularly with statistical and machine learning models. Emphasise your proficiency in Python and any experience with sports data, as this is crucial for the role.
Craft a Compelling Cover Letter: In your cover letter, express your passion for sports and how it aligns with the company's mission. Discuss specific projects where you've successfully implemented data science solutions, showcasing your problem-solving skills and creativity.
Showcase Technical Skills: Clearly outline your technical skills related to the job description, such as experience with Kubernetes, Kafka, and MLOps principles. Provide examples of how you've used these tools in past projects to demonstrate your capability.
Highlight Collaboration Experience: Since the role involves working closely with various teams, mention any previous experiences where you collaborated with product managers, developers, or QA teams. This will show that you can effectively communicate and work within a team environment.
How to prepare for a job interview at DraftKings
✨Showcase Your Technical Skills
Be prepared to discuss your proficiency in Python and any other programming languages you know. Highlight specific projects where you've built statistical or machine learning models, especially in the context of sports.
✨Demonstrate Your Understanding of Sports Data
Research the specific sports relevant to the role and be ready to discuss their rules and specifications. This will show your genuine interest and understanding of the domain, which is crucial for a Senior Data Science Engineer.
✨Emphasise Collaboration Experience
Since the role involves working closely with product teams, developers, and QAs, share examples of past experiences where you successfully collaborated on projects. Highlight how you contributed to moving projects from ideation to deployment.
✨Prepare for Problem-Solving Questions
Expect to face technical challenges during the interview. Brush up on your problem-solving skills and be ready to walk through your thought process when tackling complex data science problems, particularly those related to sports modelling.
