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For Companies At a Glance
- Tasks: Build and deploy data science solutions to enhance sports and entertainment experiences.
- Company: Join DraftKings, a leader in digital sports entertainment and gaming innovation.
- Benefits: Enjoy a collaborative culture, cutting-edge technology, and opportunities for growth.
- Why this job: Tackle rewarding challenges while shaping the future of responsible gaming.
- Qualifications: Strong background in data science, machine learning, and programming languages like Python or R.
- Other info: Be part of a dynamic team driving automation in our Sportsbook.
The predicted salary is between 28800 - 48000 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.
The Crown Is Yours
As a Data Science Engineer, you\’ll utilize data, machine learning, and software development skills to craft high-impact best-in-class sports models that grow the business. Our team comprises sports modeling experts and data science technologists, coming together to develop innovative Data Science products that drive measurable value across our Sportsbook platform.
What you\’ll do as a Data Science Engineer
- Create statistical and machine learning models and integrate them into DS applications
- Expand the capabilities of our sport models with enhanced granularity
- Writing production quality code to deploy and run models in a sportsbook platform
- Utilise our MLOps platform to train and productionise ML models
- Create automatic tests to ensure model accuracy
- Collaborate closely with product, developers, QAs, and delivery leads to move projects from ideation to development and deployment
- Test that the data flows work as expected and that models are well integrated into the larger business context
What you\’ll bring
- Strong proficiency in Python
- Experience building statistical or machine learning models for sports
- Solid understanding of data science and statistical modeling principles
- Experience with Kubernetes and Kafka is desirable
- Knowledge of MLOps principles and related tools will be considered an asset
- Familiarity with version control concepts
- Understanding of object-oriented programming principles
- PhD, Masters or Bachelor’s degree in Statistics, Data Science, Mathematics, Computer Science, Engineering, or related field
- Keen interest in sports
- Understanding of Sportsbook products is desirable
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.
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Data Science Engineer employer: DraftKings
Contact Detail:
DraftKings Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Science Engineer
✨Tip Number 1
Familiarize yourself with the latest trends in data science and machine learning, especially as they relate to the gaming industry. This knowledge will not only help you understand the challenges we face but also allow you to contribute innovative ideas during discussions.
✨Tip Number 2
Showcase your experience with low latency systems and automated trading solutions. Be prepared to discuss specific projects where you've implemented these technologies, as this will demonstrate your hands-on expertise and problem-solving skills.
✨Tip Number 3
Highlight your collaborative experiences in cross-functional teams. We value teamwork highly, so sharing examples of how you've successfully worked with others to integrate data science solutions will set you apart.
✨Tip Number 4
Prepare to communicate complex technical findings in a clear and concise manner. Practice explaining your past projects and insights to non-technical stakeholders, as this skill is crucial for supporting data-driven decision-making within our organization.
We think you need these skills to ace Data Science Engineer
Some tips for your application 🫡
Understand the Role: Make sure to thoroughly read the job description for the Data Science Engineer position. Understand the key responsibilities and required skills, especially in machine learning, statistical modeling, and programming languages like Python or R.
Tailor Your CV: Customize your CV to highlight relevant experience in data science projects, particularly those involving automation and decision-making systems. Emphasize your proficiency with tools like Kubernetes, Kafka, and Databricks if applicable.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for sports and entertainment technology. Discuss how your background aligns with the company's mission and how you can contribute to their innovative projects.
Showcase Problem-Solving Skills: In your application, provide examples of how you've tackled complex problems in previous roles. Highlight your ability to work collaboratively and deliver high-quality results under pressure.
How to prepare for a job interview at DraftKings
✨Showcase Your Technical Skills
Be prepared to discuss your experience with programming languages like Python or R. Highlight specific projects where you developed statistical models or machine learning algorithms, and be ready to explain the impact of your work.
✨Demonstrate Problem-Solving Abilities
Expect questions that assess your problem-solving skills. Use the STAR method (Situation, Task, Action, Result) to structure your responses, showcasing how you broke down complex problems and delivered high-quality results.
✨Emphasize Collaboration
Since the role involves working with cross-functional teams, share examples of how you've successfully collaborated in the past. Discuss how you communicated technical findings to non-technical stakeholders to support data-driven decision-making.
✨Familiarize Yourself with Relevant Tools
If you have experience with tools like Kubernetes, Kafka, or Databricks, make sure to mention it. Even if you haven't used them extensively, showing a willingness to learn and adapt can set you apart from other candidates.
