Senior Applied AI Data Scientist (Oncology)

TL;DR

Senior Applied AI Data Scientist (Oncology): Applying machine learning and LLMs to extract clinically relevant information from unstructured medical records with an accent on model validation, data quality, and scientific insight generation. Focus on building next-generation research data products, collaborating with clinical stakeholders, and productionizing AI systems to improve cancer care.

Location: Must be based in Tokyo (Onsite)

Company

A healthtech company and Roche Group affiliate dedicated to improving cancer care by transforming real-world patient experiences into actionable clinical evidence.

What you will do

• Apply internal and external AI systems to transform raw clinical data into high-quality research datasets.
• Collaborate with oncologists and research scientists to validate model performance and ensure scientific soundness.
• Define requirements for training and evaluation datasets while maintaining software pipelines.
• Provide analytical support and create custom data outputs for cross-functional clinical teams.
• Work with software engineers to productionize, scale, and monitor AI models.
• Act as a subject matter expert on data capabilities to drive model development and deployment.

Requirements

• 6+ years of experience as an applied data scientist or in a similar technical role.
• Proficiency in English and ability to read Japanese.
• Strong experience with Python (pandas, polars) and SQL.
• Proven experience working with ML or LLMs in a production environment.
• Ability to analyze system performance, identify quality improvements, and communicate impact to stakeholders.
• Experience mentoring technical team members on data science methods.

Nice to have

• Experience working with healthcare data.
• Experience with dbt or other ETL frameworks.

Culture & Benefits

• Opportunity to work in a mission-driven startup environment within a larger global organization.
• Collaborative, cross-functional team culture involving oncologists, engineers, and researchers.
• Focus on making a tangible impact on cancer research and patient outcomes.
• Emphasis on candid feedback and creative problem-solving.