Senior Data & Analytics Engineer

Statistical Modeling & Machine Learning Apply advanced statistical methods and prototype predictive ML models to address healthcare challenges like patient risk stratification, readmission rates, and pharmacy supply chain optimization. You will be responsible for building robust data infrastructure to handle complex clinical and pharmacy datasets, architecting scalable data pipelines, developing predictive models, and optimizing our cloud data stack.

Job Title: Senior Data & Analytics Engineer
[FG Posting: Data Scientist 3]
JP 2896 - John Hou

Reason for Opening: New
Duration: 6 months
Location: Onsite
Shift hours: M-F, can be flexible with hours but prefer 8am - 5pm, 9am - 6pm
Interview process: It will depend on location of the candidates. For local candidates it will be onsite.

Job Overview
We are seeking a Senior Data & Analytics Engineer with specialized expertise in Healthcare Data, Machine Learning (ML), and Cloud Data Environments. In this role, you will sit at the intersection of health informatics, data engineering, and advanced analytics. You will be responsible for building robust data infrastructure to handle complex clinical and pharmacy datasets, architecting scalable data pipelines, developing predictive models, and optimizing our cloud data stack. The ideal candidate is a proactive builder with a strong mathematical and analytical background who can engineer reliable data foundations while maintaining strict healthcare compliance.

Key Responsibilities
Healthcare Data Engineering & Pipeline Architecture

• Design, build, and maintain scalable, fault-tolerant ETL/ELT pipelines to ingest large volumes of structured and unstructured healthcare data.
• Standardize complex data sources including Electronic Health Records (EHR), pharmacy claims, medical billing (ICD-10, CPT, NDC codes), and clinical trial data.

Statistical Modeling & Machine Learning

• Apply advanced statistical methods and prototype predictive ML models to address healthcare challenges like patient risk stratification, readmission rates, and pharmacy supply chain optimization.
• Build and maintain operational-grade machine learning and predictive modeling pipelines, including data processing, model training, inference, and performance monitoring — all in a scalable, reproducible, and well-documented manner.
• Deploy and productionize ML pipelines, integrating models directly into healthcare software applications or analytical databases.
• Collaborate on MLOps workflows to automate model retraining, monitoring, and validation against shifting clinical data distributions.

Medical Business Intelligence & Reporting

• Build interactive dashboards and self-service analytics tools using BI platforms like Tableau DBx Dashboard.
• Translate complex data pipelines and algorithmic outcomes into clear, actionable clinical or operational insights for healthcare executives and practitioners.

Qualification Requirements
Minimum Qualifications

• Education: Bachelor's degree in Statistics, Data Science, Biostatistics, Computer Science, or a highly quantitative field.
• Experience: 5+ years of experience in data engineering, data analytics, or data science, with at least 2+ years explicitly focused on building production-grade data pipelines.
• Domain Expertise: Minimum 2 years of hands-on experience working with healthcare, pharmacy, or medical data systems and standard medical terminologies (e.g., RxNorm, LOINC, SNOMED-CT).
• Cloud Infrastructure: Hands-on experience architecting data solutions within major cloud environments (AWS, GCP, or Azure).
• Core Languages: Expert-level mastery of SQL and Python (including packages like pandas, NumPy, and scikit-learn)

Preferred Qualifications

• Advanced Degree: Master's degree or PhD in Statistics or Data Science.
• Advanced Tools: Experience with distributed computing frameworks like Apache Spark or Databricks.
• MLOps & CI/CD: Familiarity with modern MLOps tools (e.g., MLflow, AWS SageMaker) and version control/deployment workflows (Git, CI/CD pipelines).