Data Scientist Jobs

About The Position

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Computational Linguistics, Statistics, or a related quantitative field.
• 4–6 years of applied ML experience, with at least 2 years in production NLP or LLM development.
• Demonstrated ability to take models from experimentation through deployment and ongoing monitoring.
• Experience in healthcare, managed care, or regulated data environments preferred; familiarity with HIPAA data handling required.
• Strong Python: pandas, NumPy, scikit-learn; deep learning frameworks (PyTorch and/or TensorFlow).
• Proficiency in gradient boosting methods (XGBoost, LightGBM), classification modeling, and model evaluation including cross-validation, precision-recall optimization, and SHAP-based explainability.
• Hands-on NLP experience with spaCy, scispaCy, MedSpaCy, Hugging Face Transformers (BERT, GPT), and LangChain; experience with LLM fine-tuning, RAG, and vector databases.
• Familiarity with speech ML tools: ASR models (Whisper, Azure Speech), speaker diarization, and sentiment and emotion detection.
• Experience with Databricks (Delta Lake, MLflow, Spark) and AWS (S3, SageMaker, Bedrock); working knowledge of Docker and CI/CD for ML pipelines.
• Solid SQL skills; ability to work across data lakehouse and relational environments.
• Clear and precise communicator; able to document and explain model behavior to both technical and non-technical stakeholders.
• Collaborative team contributor; receptive to feedback, proactive in design discussions, and committed to shared engineering standards.
• Intellectually curious and self-directed; actively keeps pace with developments in applied ML and clinical AI.

Responsibilities

• Build and deploy supervised classification models (XGBoost, LightGBM, regularized regression) for clinical risk stratification and caregiver engagement scoring, including feature engineering, class imbalance handling, model calibration, and threshold optimization.
• Develop NLP pipelines for clinical text extraction from nurse notes and operational data — including named entity recognition, negation detection, relation extraction, and document classification using spaCy, scispaCy, and MedSpaCy.
• Implement and maintain LLM-powered tools including RAG architectures, prompt engineering, embedding models and vector search, hallucination detection, and response evaluation and guardrails for clinical and caregiver-facing use cases.
• Support speech and audio ML pipelines — ASR fine-tuning, speaker diarization, sentiment and emotion detection, and keyword detection — for real-time and ambient listening applications.
• Contribute to composite risk scoring and ensemble model fusion, including SHAP-based explainability, model calibration, and uncertainty quantification supporting care team and payer-facing outputs.
• Apply model explainability (SHAP, LIME), monitor for data drift and bias, and uphold HIPAA-compliant data handling standards throughout the ML lifecycle.
• Maintain MLOps standards: implement automated testing, CI/CD for ML, model versioning via MLflow, and participate in experiment tracking and model registry governance in Databricks.
• Collaborate with Data Engineering on feature store integration, scoring pipeline design, and structured output schemas; participate in technical design reviews and provide peer feedback.
• Perform other duties and special projects as assigned.

Benefits

• Flexible schedules
• Remote-first culture
• Nationally recognized wellness program