Machine Learning Engineer

We are building a small, high-impact team to support advanced analytics and machine learning initiatives for a leading healthcare technology management company. This team will focus on predictive modeling, cost optimization, and ROI analysis for clinical asset management and capital planning.

Key Responsibilities

• Develop and deploy statistical and machine learning models for predictive maintenance, resource optimization, and operational efficiency.
• Perform econometric and financial analysis to support capital planning and cost-benefit decisions.
• Design and implement data pipelines for large-scale healthcare datasets (EHR, claims, RTLS, device telemetry).
• Collaborate with cross-functional teams (clinical engineering, finance, IT) to translate insights into actionable strategies.
• Ensure compliance with HIPAA and healthcare data governance standards.

Required Qualifications

• Master's or Ph.D. in Statistics, Economics, Data Science, or related field.
• 3+ years of experience in ML model development and deployment.
• Strong foundation in statistical inference, econometrics, and causal analysis (e.g., regression, Bayesian methods, DiD).
• Proficiency in Python, SQL, and ML frameworks (Scikit-Learn, XGBoost, TensorFlow).
• Excellent communication skills for presenting insights to technical and business stakeholders.

Preferred Skills

• Experience with healthcare data (EHR, claims, RTLS).
• Familiarity with capital planning and ROI modeling.
• Knowledge of cloud platforms (AWS, Azure) and containerized deployments.