Senior Data Scientist

Our client is developing a Patient Risk Scoring solution aimed at improving healthcare outcomes through predictive analytics. The project focus on building machine learning models to identify high-risk patients (e.g., risk of non-compliance, medication refusal, or health deterioration) and enable timely clinical interventions.

Requirements

• Education: Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Bioinformatics, Health Informatics, or a related quantitative field.
  
• Professional Experience: 5+ years of experience developing and deploying machine learning solutions in production environments, preferably within healthcare or clinical data ecosystems.
  
• Microsoft Fabric Experience: Hands-on experience building and deploying data and ML workflows within the Microsoft Fabric ecosystem (OneLake, Notebooks, Spark, Data Factory).
  
• Machine Learning Proficiency: Strong grasp of classical machine learning algorithms (e.g., XGBoost, Random Forests, Logistic Regression) and modern deep learning techniques, specifically for tabular and time-series data.
  
• Programming Skills: Advanced proficiency in Python and SQL. Experience with data manipulation and ML libraries (Pandas, PySpark, Scikit-Learn, PyTorch, or TensorFlow).
  
• Communication: Excellent ability to translate complex technical and statistical concepts to non-technical clinical and business stakeholders.
• English level: Upper – Intermediate

Nice to have:

• NLP Experience: Experience using Natural Language Processing (NLP) or Large Language Models (LLMs) to extract features from unstructured clinical notes.
• Cloud Certifications: Relevant Microsoft Azure or Fabric certifications (e.g., Microsoft Certified: Fabric Analytics Engineer Associate, Azure Data Scientist Associate).

Responsibilities

• Model Development & Deployment: Design, train, evaluate, and deploy machine learning models to predict patient risk scores (e.g., medication refusal, non-compliance, decompensation etc.).
• EHR Data Engineering & Processing: Extract, clean, and transform healthcare data from EHR systems (e.g., Credible) to build robust feature sets for predictive modeling.
• End-to-End Analytics with Microsoft Fabric: Utilize Microsoft Fabric’s unified analytics platform (including Data Engineering, Data Science, and Real-Time Analytics workloads) to orchestrate data pipelines, manage Lakehouse architectures, and scale ML training/inference.
• Clinical Collaboration: Partner closely with clinical stakeholders, medical officers, and care teams to define risk cohorts, ensure the clinical validity of model features, and translate model outputs into actionable clinical workflows.
• MLOps & Monitoring: Establish continuous integration, deployment, and monitoring of ML models to track data drift, model degradation, and fairness/bias over time.
• Compliance & Privacy: Ensure all data handling and modeling practices strictly adhere to healthcare regulations (e.g., HIPAA, HITRUST) and maintain the highest standards of data security and patient privacy.

Working conditions:

Mon – Fri 9-5 (EST) overlap with team at least 4 hours