Staff AI Researcher - ML and AI modeling in Epidemiology

Primary Duties:

• Train and fine-tune models using off-the-shelf and novel ML/AI techniques solving optimization problems for the company.
• Work with large, complex data sets. Conducting difficult, non-routine analysis and harvesting data. 
• Deliver working POC solutions solving speed, scalability and time-to-market tradeoffs.

Minimum Qualifications:

• A Bachelor's degree (BA/BS/BTech) in a public health or quantitative field such as Statistics, Biostatistics,  Data Science, Applied Math or Computer Science is required.
• 8+ years of relevant statistical analysis experience including predictive modeling.
• 8+ years of relevant machine learning experience (ML modeling, hyperparameter tuning, feature engineering, model validation etc).
• Background in Epidemiology, particularly use of epidemiologic principles to guide feature engineering and model interpretation across a variety of chronic conditions.
• 5-7 years of experience selecting, implementing, and optimizing ML tools and frameworks for large-scale projects.
• 3+ years of Python language experience.
• 2+ years of relevant deep learning and LLM experience.
• 2+ years experience working with large-scale distributed systems at scale and statistical software (e.g. Spark).
• Experience in addressing challenges from incomplete, unrepresentative, and mislabeled data.
• Track record of significant contributions to the field (e.g., publications, patents, or successful large-scale implementations).

Preferred KSA’s:

• A Ph.D. or Master's degree in Epidemiology, Biostatistics, or a similar health-data field is strongly preferred. We also welcome candidates from other quantitative disciplines like Statistics, Computer Science, Operations Research, Economics, and Mathematics, especially with equivalent practical experience.
• Working knowledge of the U.S. healthcare system and its financing, with a focus on Value-Based Care and Risk adjustment.
• Working knowledge of health-tech systems, such as Electronic Health Records and clinical data.
• Proficiency in communicating analysis and establishing confidence among audiences who do not share your disciplinary background or training.
• Experience with security and systems that handle sensitive data.
• Experience working with statistical software (e.g. R, SAS, Python statistical packages).
• Demonstrated leadership and self-direction. 
• First-author publications in  peer-reviewed journals and presentations at professional meetings (e.g. NeurIPS, ICML, ACL, JSM, KDD, EMNLP).
• Winners in ACIC Data Challenge, Kaggle etc.

Physical Requirements:

• Sitting for prolonged periods of time. Extensive use of computers and keyboard. Occasional walking and lifting may be required.