REMOTE- Data Scientist (Healthcare) || H1B Transfer,|| W2

About the Role: We're looking for a hands-on Data Scientist to solve real-world business problems in healthcare/health insurance using machine learning and modern cloud tooling. You'll own the full lifecycle-from problem framing and data wrangling to modeling, deployment support, and stakeholder storytelling.

Top Skills: Python/R, SQL, Spark/Databricks, Azure ML or Vertex AI, MLflow, Git/GitHub, Jupyter/Databricks notebooks, Power BI/Tableau, Azure/Google Cloud Platform services.

What You'll Do:

• Partner with business/clinical stakeholders to define measurable use cases (e.g., risk & cost forecasting, readmission/LOS prediction, member churn, fraud/waste/abuse).
• Explore, cleanse, and engineer tabular data from claims, eligibility, EMR, utilization, and care-management sources.
• Build and evaluate models using regression, classification, time series, clustering, trees/GBMs; pilot deep-learning where additive value.
• Productionize with data & MLOps teams: feature pipelines (Spark/Databricks), model packaging, monitoring, drift/decay, and A/B testing.
• Write clear analyses, dashboards, and exec-ready presentations; translate findings into actions and ROI.
• Ensure privacy/compliance (HIPAA/PHI), data governance, and reproducible research (git, notebooks, experiment tracking).
• Contribute to LLM initiatives: prompt engineering, RAG, basic fine-tuning, and experiments with agentic AI frameworks for workflow automation.

Required Qualifications

• 6 8 years of hands-on data science delivering business impact (healthcare/health insurance experience is a big plus).
• Strong tabular data handling/manipulation skills.
• Solid SQL and proficiency in at least one statistical programming language (Python or R).
• Working knowledge of MS Office (Excel/PowerPoint/Access) for quick analyses and stakeholder comms.
• Experience with Big Data tools: HDFS, Hive, Spark, MapR-DB (or equivalent).
• Practical experience with statistical & ML techniques: linear/logistic regression, time series, clustering, decision trees, tree ensembles (XGBoost/LightGBM).
• Cloud & ML platforms: Databricks, Azure ML and/or Google Vertex AI (pipelines, model registry, deployment workflows).
• Exposure to prompt engineering, LLM fine-tuning (LoRA/PEFT or platform-native), and agentic AI concepts.
• Clear, concise communicator; able to convert ambiguity into structured analysis and decisions.
• BS/MS in Computer Science, Statistics, Applied Math, Engineering, or related field (or equivalent experience).