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 in London employer: National Geographic
At DraftKings, we pride ourselves on being an exceptional employer that fosters innovation and collaboration within our Trading Intelligence Team. Our vibrant work culture encourages creativity and continuous learning, offering ample opportunities for professional growth and mentorship, particularly for those in data science roles. Located in a dynamic environment, we empower our employees to leverage cutting-edge AI technologies while enjoying a supportive atmosphere that values their contributions and promotes a healthy work-life balance.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Science Engineer in London
✨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 skills and problem-solving abilities. Practice explaining your thought process clearly, as communication is key when discussing complex data science concepts with non-technical stakeholders.
✨Tip Number 4
Don’t forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Plus, it’s a great way to ensure your application gets the attention it deserves.
We think you need these skills to ace Senior Data Science Engineer in London
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 programming skills in Python or R. Include specific examples of how you've used these languages to solve complex problems or develop data-driven solutions.
Be Clear and Concise:Keep your application clear and to the point. Use bullet points where possible to make it easy for us to see your key achievements and skills at a glance. Remember, we love a well-structured application!
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s the easiest way for us to keep track of your application and ensure it reaches the right people!
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 algorithms you used, and the outcomes. 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 or discuss your approach to data manipulation and visualisation. Practising coding challenges beforehand can really help you feel more confident.
✨Collaboration is Key
This role involves working with cross-functional teams, so be ready to talk about your experience collaborating with others. Share examples of how you’ve integrated data science solutions into production systems and how you’ve communicated technical findings to non-technical stakeholders.
✨Mentorship Matters
As a senior position, mentoring junior data scientists is part of the job. Think about your past experiences in guiding others and be prepared to discuss your mentoring style. Highlight any best practices or methodologies you’ve shared and how they’ve helped your team grow.
