Offer summary

Qualifications:

Bachelor's degree in a health-related or quantitative field such as Statistics, Biostatistics, Data Science, Applied Math, or Computer Science., At least 8 years of experience in statistical analysis and machine learning, including predictive modeling., Strong background in Epidemiology and experience with large-scale data analysis., Proficiency in Python and experience with deep learning and distributed systems..

Key responsibilities:

Develop and fine-tune ML and AI models using medical data.

Analyze large, complex datasets to extract insights.

Create proof-of-concept solutions addressing scalability and speed.

Collaborate with engineering and analytics teams to integrate AI into products.

Job description

As a Staff AI Researcher, you will develop ML and AI solutions that will improve health for millions of people. Here at Aledade we empower primary care physicians with technology to keep their patients healthy and prevent unnecessary hospitalizations. You will partner with other engineering and analytics teams, bringing AI technology into existing products and workflows.

As a Staff AI Researcher, you will lead the way to harness knowledge from one of the most extensive data sets of medical records, diagnoses, claims, and prescriptions. You will have a unique opportunity to train, finetune and use AI models using medical data we collect from millions of patients across the country.

Primary Duties:Train and finetune models using offtheshelf and novel MLAI techniques solving optimization problems for the company.
Work with large, complex data sets. Conducting difficult, nonroutine analysis and harvesting data.
Deliver working POC solutions solving speed, scalability and timetomarket tradeoffs.

Minimum Qualifications:
A Bachelors degree (BABSBTech) 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.
57 years of experience selecting, implementing, and optimizing ML tools and frameworks for largescale projects.
3+ years of Python language experience.
2+ years of relevant deep learning and LLM experience.
2+ years experience working with largescale 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 largescale implementations).

Preferred KSA’s:
A Ph.D. or Masters degree in Epidemiology, Biostatistics, or a similar healthdata 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 ValueBased Care and Risk adjustment.
Working knowledge of healthtech 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 selfdirection.
Firstauthor publications in peerreviewed 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.