Job Description
We're revolutionizing healthcare analytics and need a visionary Senior Data Scientist to join our mission-driven team. You'll architect cutting-edge solutions that transform raw data into life-saving insights, directly impacting patient outcomes across our global network.
As a key strategic partner, you'll collaborate with C-suite executives, clinical researchers, and engineering leaders to design scalable ML pipelines. Our culture champions innovation through experimentation, and we provide industry-leading resources including GPU clusters and real-world healthcare datasets spanning 10M+ patient records.
This hybrid role offers competitive equity, comprehensive benefits including family healthcare coverage, and dedicated professional development funds. If you're passionate about leveraging data to create tangible social impact, we want to hear from you.
Responsibilities
- Design and implement end-to-end machine learning solutions for predictive analytics in healthcare operations
- Lead A/B testing frameworks for clinical trial optimization, reducing patient recruitment time by 30%
- Develop real-time anomaly detection systems for hospital resource allocation
- Create executive-level data visualizations using Tableau and Power BI to drive strategic decisions
- Mentor junior scientists through our internal 'Data Guild' program
- Publish findings in peer-reviewed journals and industry conferences
- Architect data lakes on AWS/GCP with automated ETL pipelines processing 5TB+ daily
Qualifications
- PhD in Statistics, Computer Science, or related field with 5+ years industry experience
- Expertise in Python (scikit-learn, TensorFlow) and SQL with proven production deployment
- Strong portfolio demonstrating healthcare analytics projects (HIPAA-compliant preferred)
- Advanced knowledge of causal inference methods and experimental design
- Experience with distributed computing (Spark, Dask) and cloud platforms
- Published research in top-tier ML conferences (NeurIPS, ICML) or healthcare journals
- Certification in healthcare data privacy (e.g., CISSP, CPHIMS)