Staff AI Researcher

Employment Type: Full Time

Position Overview

As a Staff AI Researcher at Aledade, you will develop Machine Learning (ML) and Artificial Intelligence (AI) solutions aimed at improving the health of millions of people. Aledade empowers primary care physicians with technology to keep their patients healthy and prevent unnecessary hospitalizations. You will collaborate with other engineering and analytics teams, integrating AI technology into existing products and workflows. This role offers a unique opportunity to lead the harnessing of knowledge from one of the most extensive datasets of medical records, diagnoses, claims, and prescriptions, enabling you to train, fine-tune, and utilize AI models with medical data collected from millions of patients nationwide.

Primary Duties

• Train and fine-tune models using off-the-shelf and novel ML/AI techniques to solve company optimization problems.
• Work with large, complex datasets, conducting difficult, non-routine analysis and harvesting data.
• Deliver working Proof of Concept (POC) solutions, balancing speed, scalability, and time-to-market tradeoffs.

Minimum Qualifications

• Education: BA/BTech in Statistics, Data Science, Computer Science, or a related field.
• Experience:
  • 8+ years of relevant statistical analysis experience.
  • 8+ years of relevant machine learning experience (ML modeling, hyperparameter tuning, feature engineering, model validation, etc.).
  • Background in Epidemiology, particularly in the context of chronic condition modeling.
  • 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 of experience working with large-scale distributed systems and statistical software (e.g., Spark).
  • Experience in addressing challenges arising from incomplete, unrepresentative, and mislabeled data.
  • Track record of significant contributions to the field (e.g., publications, patents, or successful large-scale implementations).

Preferred Qualifications (KSA's)

• Education: Ph.D. or Master's degree in a quantitative discipline (e.g., Computer Science [with AI/ML Major], Statistics, Operations Research, Economics, Mathematics, Physics) or equivalent practical experience.
• Knowledge:
  • Working knowledge of Public Health, with a focus on Value-Based Care and Risk Adjustment.
  • Working knowledge of health-tech systems, such as Electronic Health Records and clinical data.
• Skills:
  • Proficiency in communicating analysis and establishing confidence among audiences with diverse disciplinary backgrounds.
  • 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.
• Achievements:
  • First-author publications at peer-reviewed conferences (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.