Lead Medical Data Scientist — EMR Analytics & Client Impact

Latica; Palo Alto, Northern (CA, KY); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Latica, based in Palo Alto, seeks a Data Scientist to lead end-to-end medical research projects for top life science clients. You will apply advanced analytics to de-identified clinical data and drive insights that inform diagnostics and therapeutics.

You will perform EMR data exploration, feature engineering with LLMs, and support clinical protocol design from a data perspective, presenting results to clients via dashboards and publications. Collaboration and rapid learning are essential.

Qualifications

• 5+ years as Data Scientist or similar role in healthcare.
• Experience working with pharmaceutical or life sciences clients.
• Advanced proficiency in SQL.
• Strong experience with Python.
• Deep familiarity with Electronic Medical Records (EMRs) and healthcare coding systems (CPT, ICD-10, etc.).
• Experience with cloud infrastructure platforms (AWS/GCP) is preferred.

Responsibilities

• Lead end-to-end medical research projects with top-tier life science companies.
• Conduct EMR data exploration and extract relevant clinical features.
• Apply LLM-based feature engineering to transform unstructured data into clinical insights.
• Support clinical protocol design from a data and feasibility perspective.
• Perform statistical analyses and present results to clients (dashboards, publications, presentations).
• Become a subject matter expert in Latica's proprietary healthcare data.
• Develop cross-client pipelines to support data infrastructure and product.

Skills

SQL

Python

EMR knowledge

Data analysis

Healthcare domain

Job description

Latica, based in Palo Alto, seeks a Data Scientist to lead end-to-end medical research projects for top life science clients. You will apply advanced analytics to de-identified clinical data and drive insights that inform diagnostics and therapeutics.

You will perform EMR data exploration, feature engineering with LLMs, and support clinical protocol design from a data perspective, presenting results to clients via dashboards and publications. Collaboration and rapid learning are essential.