At a Glance
- Tasks: Lead data science projects and develop machine learning models to solve real-world problems.
- Company: Join DraftKings, a leader in AI-driven innovation and customer experience.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative team environment with mentorship opportunities and cutting-edge technology.
- Why this job: Make an impact in the exciting world of trading intelligence and AI.
- Qualifications: Experience in data science, machine learning, and programming languages like Python or R.
The predicted salary is between 60000 - 80000 £ per year.
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.
Responsibilities
- Lead data science projects from conception to deployment, ensuring high‑quality and timely delivery.
- Develop and implement statistical models and machine learning algorithms to solve complex business problems.
- Collaborate with cross‑functional teams to integrate data science solutions into production systems.
- Mentor junior data scientists and provide guidance on best practices and methodologies.
- Communicate technical findings and insights to internal stakeholders to support data‑driven decision‑making.
- Assist with the adoption of data‑driven strategies into the trading processes.
- Assist with the design, development, maintenance, and testing strategy of trading automation solutions, ensuring alignment with overall business objectives.
Qualifications
- Proven experience in data science, with a strong foundation in machine learning and statistical modeling.
- Proficiency in programming languages such as Python or R, and experience with data manipulation and visualization tools.
- Demonstrated ability to break down complex problems into manageable tasks and deliver high‑quality results.
- Excellent problem‑solving skills and the ability to work collaboratively in a team environment.
- Experience in developing and implementing automated trading or decision‑making systems (highly desirable).
- Experience with Kubernetes and Kafka (desirable).
- Experience with Databricks (desirable).
- Experience with experimentation (desirable).
- A Bachelor's degree in a relevant field such as Computer Science, Statistics, Mathematics, or a related discipline.
Senior Data Science Engineer employer: National Geographic
At DraftKings, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. Our Senior Data Science Engineers play a pivotal role in shaping the future of AI in trading intelligence, with ample opportunities for professional growth and mentorship. Located in a vibrant environment, we provide our employees with the tools and support needed to thrive, ensuring that every team member can contribute to meaningful projects while enjoying a fulfilling career.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Science Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data science projects, especially those involving machine learning and statistical models. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice explaining your past projects and how you tackled complex problems, as this will help you communicate effectively with interviewers.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace Senior Data Science Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV speaks directly to the role of Senior Data Science Engineer. Highlight your experience with machine learning and statistical modelling, and don’t forget to mention any relevant projects you've led or contributed to.
Showcase Your Skills:When writing your application, be sure to showcase your proficiency in Python or R. Include specific examples of how you've used these languages to solve complex problems or develop data-driven solutions.
Communicate Clearly:Remember, we want to see how you communicate technical findings. Use clear and concise language to explain your past projects and the impact they had on decision-making processes. This will show us your ability to convey complex ideas effectively.
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you're keen on joining our team!
How to prepare for a job interview at National Geographic
✨Know Your Stuff
Make sure you brush up on your data science fundamentals, especially machine learning and statistical modelling. Be ready to discuss specific projects you've worked on, the challenges you faced, and how you overcame them. This will show that you not only understand the theory but can also apply it in real-world scenarios.
✨Showcase Your Coding Skills
Since proficiency in Python or R is key for this role, be prepared to demonstrate your coding skills. You might be asked to solve a problem on the spot, so practice coding challenges beforehand. Familiarise yourself with data manipulation and visualisation tools as well, as these will likely come up in conversation.
✨Collaboration is Key
Highlight your experience working with cross-functional teams. Be ready to share examples of how you've collaborated with others to integrate data science solutions into production systems. This will show that you can work well in a team environment, which is crucial for this role.
✨Communicate Clearly
You’ll need to communicate technical findings to non-technical stakeholders, so practice explaining complex concepts in simple terms. Think about how you can convey your insights effectively, as this will demonstrate your ability to support data-driven decision-making within the company.
