Staff Data Scientist, Machine Learning in Epidemiology and Patient Data Products (AI)

Location: Remote within the United States, or offices in Lexington, Massachusetts and San Francisco, California. Permanent US work authorization is required; visa sponsorship is not available.

Salary: $165,000–$190,000 USD remote; $175,000–$220,000 USD in California.

Company

hirify.global is an AI-enabled biotechnology company developing medicines through real-world data, artificial intelligence, human translational models, and predictive chemistry.

What you will do

• Lead the development of machine learning methods and analyses for real-world patient data with epidemiology, biology, and other stakeholders.
• Analyze and model high-dimensional longitudinal data from EHRs, clinical notes, sequencing, and multi-omics sources in cloud environments.
• Design, implement, and evaluate machine learning approaches that generate novel clinical insights and patient profiles.
• Build, maintain, and operationalize ML pipelines and translate model outputs for diverse audiences.
• Break down ambiguous problems, prioritize critical-path work, and contribute to coding standards, code reviews, and reproducible analyses.
• Collaborate with multidisciplinary project teams and facilitate meetings and technical discussions.

Requirements

• MS, MPH, or PhD in health data science, biostatistics, or a related quantitative field, plus 5 years of experience applying ML methods.
• At least 3 years of direct experience working with real-world patient data and healthcare databases, including EHRs, claims, or patient registries.
• Experience with medical coding ontologies and data models such as ICD, ATC, LOINC, SNOMED, CPT, HCPCS, and OMOP.
• Proficiency across supervised, unsupervised, semi-supervised, regression, classification, tree-based, clustering, dimensionality-reduction, and deep learning methods.
• Mastery of Python and modern data science tools, including scikit-learn, PyTorch, statsmodels, SciPy, MLlib, or MLflow.
• Permanent US work authorization without current or future sponsorship is required.

Nice to have

• Experience in biopharmaceutical, epidemiological, or biostatistical settings.
• Experience processing clinical notes, representation learning, transformer-based sequence models, or AI-assisted coding tools.
• Knowledge of causal methods for observational studies, including propensity scores, bias adjustment, and covariate selection.
• Familiarity with traditional drug discovery and development processes.

Culture & Benefits

• Work in an innovative startup environment with cross-functional, multidisciplinary teams.
• Contribute to patient-centric innovation and the development of new medicines.
• Healthcare coverage, an annual incentive program, retirement benefits, and additional benefits are provided.
• Work remotely within the United States or from company offices in Lexington and San Francisco.