Offer summary

Qualifications:

Bachelor's or BTech degree in Statistics, Data Science, Computer Science, or related field., At least 6 years of experience in statistical analysis and machine learning., Strong understanding of causal inference, treatment effects estimation, and large-scale ML tools., Proficiency in Python and experience with deep learning, LLMs, and distributed systems..

Key responsibilities:

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

Analyze large and complex healthcare datasets to extract insights.

Create proof-of-concept solutions addressing speed, scalability, and deployment challenges.

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

Job description

As a Senior MLAI 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, finetune, and use AI models using medical data collected 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:
BABTech in Statistics, Data Science, Computer Science or a related field require.
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.
35 years of experience selecting, implementing, and optimizing ML tools and frameworks for largescale projects.
2+ years of Python language experience.
1+ years of relevant deep learning and LLM experience.
1+ 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.
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.
Background in Epidemiology, particularly in the context of chronic condition modeling.
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.
Publications in peerreviewed journals and presentations at professional meetings (e.g. NeurIPS, ICML, ACL, JSM, KDD, EMNLP).
Participation 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.