As a Senior AI Researcher II, you will develop ML and AI solutions that will improve health for millions of people. At Aledade, we empower primary care physicians with technology to keep their patients healthy and prevent unnecessary hospitalizations. You will collaborate with engineering and analytics teams to integrate AI technology into existing products and workflows.You will work with one of the most extensive data sets of medical records, diagnoses, claims, and prescriptions. This role offers a unique opportunity to train, fine-tune, and use AI models using medical data collected from millions of patients across the country.
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:
BA/BTech in Statistics, Data Science, Computer Science or a related field required.
6+ years of relevant statistical analysis experience.
6+ years of relevant machine learning experience (ML modeling, hyperparameter tuning, feature engineering, model validation etc)
Understanding of causal inference and treatment effects estimation.
3-5 years of experience selecting, implementing, and optimizing ML tools and frameworks for large-scale projects.
2+ years or Python language experience.
1+ years of relevant deep learning and LLM experience.
1+ 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.
Contributions to the field (e.g., publications, patents, or successful large-scale implementations).
Preferred KSA’s:
Master or PhD degree in a quantitative discipline (e.g., Computer Science[with AI/ML Major], Statistics, Operations Research, Economics, Mathematics, Physics) or equivalent practical experience.
Working knowledge of health-tech systems, like Electronic Health Records, Clinical data, etc.
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).
