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- Tasks: Use machine learning to transform cancer data into impactful research insights.
- Company: Flatiron Health, a healthtech leader improving lives through data.
- Benefits: Competitive salary, health benefits, and a supportive work culture.
- Why this job: Make a real difference in oncology by leveraging cutting-edge technology.
- Qualifications: 3+ years in ML, experience with NLP, and a passion for problem-solving.
- Other info: Join a dynamic team dedicated to advancing healthcare through innovation.
The predicted salary is between 36000 - 60000 £ per year.
Data Scientist, Machine Learning at Flatiron Health
Overview
Flatiron Health is a healthtech company using data for good to power smarter care for every person with cancer. Our multidisciplinary teams include oncologists, data scientists, software engineers, epidemiologists, product experts and more. We are an independent affiliate of the Roche Group.
What You’ll Do
At Flatiron, we are advancing the use of machine learning, generative AI, and natural language processing to extract clinically relevant information from unstructured medical notes for use in oncology research. The Discovery team is helping to build next‑generation research data products, developing and applying ML models to capture a complete picture of the patient journey.
- Interface with internal scientific stakeholders and customers to understand data needs for high‑quality research.
- Build models to turn raw clinical data into high‑quality research variables, using LLMs, traditional ML, and NLP techniques.
- Collaborate with quantitative scientists and oncologists to validate models for sound scientific insights.
- Work cross‑functionally with software engineers to productionize, scale, and monitor models.
Who You Are
- 3+ years of relevant technical experience focused on ML, with strong NLP and LLM background preferred.
- Solid grasp of statistical fundamentals of ML and experience solving real‑world problems.
- Experience with version control, Python, and SQL in a production development environment.
- Excited to work in a startup environment, creative, and scrappy to get the job done.
Extra Credit
- ML or LLM experience in a healthcare setting.
- Experience with bias, health equity research or work with underrepresented groups in clinical research.
Who We Are
Our people are at the center of everything we do. We foster a culture where teammates feel equipped and empowered to make meaningful contributions with confidence, compassion, and clarity. Visit the Life at Flatiron page to learn more.
Referrals increase your chances of interviewing at Flatiron Health by 2x.
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Data Scientist, Machine Learning employer: Flatiron Health
Contact Detail:
Flatiron Health Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist, Machine Learning
✨Tip Number 1
Network like a pro! Reach out to current employees at Flatiron Health on LinkedIn or through mutual connections. A friendly chat can give you insider info and might just get your foot in the door.
✨Tip Number 2
Show off your skills! Prepare a portfolio showcasing your machine learning projects, especially those related to healthcare. This will help us see how you can contribute to our mission of improving lives through data.
✨Tip Number 3
Ace the interview by being ready to discuss real-world applications of your work. We love candidates who can connect their experience with our goals, so think about how your past projects align with what we do at Flatiron.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team.
We think you need these skills to ace Data Scientist, Machine Learning
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Data Scientist, Machine Learning role. Highlight your experience with ML and NLP, and don’t forget to showcase any relevant projects or achievements that align with our mission at Flatiron Health.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to express your passion for using data for good and how you can contribute to improving lives through your work. Be genuine and let your personality come through!
Showcase Your Technical Skills: We want to see your technical prowess! Make sure to include specific examples of your experience with Python, SQL, and any ML models you've developed. This will help us understand how you can hit the ground running in our team.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our Flatiron family!
How to prepare for a job interview at Flatiron Health
✨Know Your ML and NLP Stuff
Make sure you brush up on your machine learning and natural language processing knowledge. Be ready to discuss specific projects where you've applied these techniques, especially in healthcare settings. Flatiron is looking for someone who can demonstrate a solid grasp of the statistical fundamentals behind ML.
✨Show Your Collaborative Spirit
Flatiron values teamwork, so be prepared to share examples of how you've worked with cross-functional teams. Highlight any experiences where you've collaborated with software engineers or oncologists to bring models from development to production. This will show that you can thrive in their multidisciplinary environment.
✨Understand the Impact of Your Work
Flatiron is all about using data for good, so convey your passion for making a difference in oncology. Discuss how your work as a data scientist can lead to real-world improvements in patient care. This will resonate well with their mission to improve lives through data.
✨Be Ready for Technical Questions
Expect some technical questions during the interview. Brush up on your Python and SQL skills, and be prepared to solve problems on the spot. They might ask you to explain your thought process when developing a model or how you would handle unstructured data, so think through these scenarios beforehand.