Particle Health is revolutionizing healthcare data analytics and interoperability by unlocking the power of medical records in an intelligent platform that focuses health back on the patient. Through our modern data platform, we enable healthcare innovators to access standardized, real-time patient data, bridging fragmented data silos to tackle critical use cases such as care coordination, risk stratification, patient identity management, and regulatory compliance.
Requirements
- Proficiency in Python and SQL skills
- Experience working with or strong interest in healthcare data (EHR, FHIR, HL7, claims)
- Experience developing analytical models or applying statistical methods to solve business problems
- Exposure to modern AI methods, such as generative models, large language models (LLMs), or applied AI systems, and understanding how to integrate them into analytical workflows
- Experience with data visualization / BI tools (e.g., Looker, Tableau, or Dash)
- Exposure to machine learning concepts and model evaluation
- Familiarity with healthcare standards such as FHIR, HL7v2, or CCDA
Responsibilities
- Build and validate analytical and machine learning models to address key healthcare data challenges (e.g., treatment adherence, gaps in care, risk scoring).
- Explore, clean, and analyze large-scale healthcare datasets to identify patterns and generate actionable insights.
- Work with Product and Engineering to translate findings into scalable, production-ready solutions.
- Contribute to projects involving AI and NLP, helping extract and summarize meaningful information from clinical text.
- Ensure statistical rigor, accuracy, and reproducibility in all analyses.
- Identify and address data quality gaps to improve completeness and reliability.
- Partner with the Data Engineering team to enhance the usability and consistency of our datasets.
Other
- 3+ years in data science, analytics, or a related role
- Curiosity, problem-solving mindset, strong communication, proactive learner
- Hybrid role based in New York City. The team works in-office two days per week (Tuesdays and Thursdays), so applicants should be within commuting distance and comfortable with in-person collaboration.
- Communicate findings clearly to both technical and non-technical audiences.
- Continuously learn, experiment, and contribute ideas to improve how we use data to drive outcomes.
