Staff Data Scientist, Machine Learning

We’re looking for a Staff Data Scientist - Machine Learning to help us accomplish our mission to improve and extend lives by learning from the experience of every person with cancer. Are you ready to be the next changemaker in cancer care?

Flatiron Health is a healthtech company using data for good to power smarter care for every person with cancer, around the world. Flatiron partners with cancer centers in the US, Europe and Asia to transform patients’ real-life experiences into real-world evidence and create a more modern, connected oncology ecosystem. Our multidisciplinary teams include oncologists, data scientists, software engineers, epidemiologists, product experts and more. Flatiron Health is an independent affiliate of the Roche Group.

What You'll Do

At Flatiron, we’re 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. Collaborating closely with our US-based teams, you will help shape our machine learning strategy for our international markets with the goal of enhancing the efficiency and scalability of our data processing methodologies.

In this role, you will lead model development projects from initial scoping to production and delivery. You will provide technical leadership by shaping roadmaps and guiding technology decision.

In addition, you'll also:

• Interface with internal scientific stakeholders and customers to understand what data they need to conduct high quality research.
• Build models to turn raw clinical data into high quality research variables, drawing on your knowledge of LLMs, traditional ML, and NLP techniques to determine the right methods to use for a given problem.
• Work with quantitative scientists and oncologists to validate that your models can be used to generate sound scientific insights.
• Collaborate with other Data Scientists to accelerate our ML capabilities and develop novel approaches to clinical data extraction from unstructured health records.
• Work cross-functionally with software engineers to productionize, scale, and monitor your models.
• Embed with our Data Product teams in the UK, Germany, and Japan to support them in the adoption of newly developed AI solutions into their processes and pipelines.
• Mentor and support career growth for other engineers on the team.

Who You Are

You're a product-focused data scientist, with experience in leveraging ML and NLP to solve real-world problems. You're excited by the prospect of rolling up your sleeves to tackle meaningful problems each and every day. You’re a kind, passionate and collaborative problem-solver who seeks and gives candid feedback, and values the chance to make an important impact.

• You are an experienced Data Scientist or Machine Learning Engineer and have 6+ years of relevant working experience, with a focus on ML. You have experience with deep learning and LLMs.
• You have a strong background in applying ML to solve real-world problems and a solid grasp of the underlying statistical fundamentals of ML.
• You are excited to work in a startup environment, think creatively and be scrappy to get the job done. You have a nose for value and empathy for your customers
• You have collaborated with other technical team members in a production development environment using formal version control, Python, and SQL.
• You have led cross-functional initiatives and excel at influencing decision-making without authority.
• You are willing to occasionally travel to our offices in Germany and Japan.

Extra Credit

• You have ML experience in a healthcare setting.
• You have experience with the risks of bias in machine learning, health equity research/analysis or have worked with underrepresented groups in a clinical research setting.

Who We Are

Our people are at the centre of everything we do. We strive to foster a culture where our teammates feel equipped and empowered to make meaningful contributions with confidence, compassion, and clarity.

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