Note: The job is a remote job and is open to candidates in USA. Careforth is seeking a Senior Data Scientist to develop and deploy predictive models, NLP solutions, and AI-powered tools for its caregiver support platform. The role focuses on identifying health and behavioral risks, supporting personalized interventions, and advancing model quality, compliance, and production ML practices.

Responsibilities

• Design, build, and deploy predictive and probabilistic models across risk stratification, behavioral engagement, composite scoring, and treatment pathway prediction owing the full lifecycle from feature engineering through production monitoring
• Develop NLP and LLM-powered pipelines that extract structured insight from unstructured clinical and operational data, including fine-tuning, RAG architecture, prompt engineering, and guardrail design for a clinical context
• Apply speech and audio ML techniques — ASR, speaker diarization, sentiment and emotion detection — to real-time and ambient listening use cases that surface signals during caregiver and coach interactions
• Build conversational AI and decision-support capabilities including intent classification, Text-to-SQL, next-best-action modeling, and causal inference frameworks that inform care interventions and payer reporting
• Define and track outcome metrics, conduct A/B testing and cohort analysis, and generate statistically rigorous performance narratives that demonstrate program effectiveness to internal and external stakeholders
• Ensure model quality, explainability, and compliance — applying SHAP/LIME, monitoring for drift and bias, and upholding HIPAA-compliant data handling standards throughout the ML lifecycle
• Partner with Data Engineering on feature store design, real-time and batch scoring pipelines, and MLflow-based experiment tracking; contribute to MLOps standards, model registry governance, and CI/CD for ML
• Mentor junior data scientists and influence the broader AI roadmap by evaluating emerging techniques and translating model outputs into actionable tools for care teams, coaches, and health plan partners

Skills

• Master's degree in Data Science, Decision Science, Statistics, Computer Science, Computational Linguistics, or a related quantitative field is required
• 7+ years of applied data science experience with a demonstrated track record of deploying predictive, NLP, and generative AI models in production environments
• Proven ability to work across the full modeling lifecycle: problem framing, feature engineering, model development, evaluation, deployment, and ongoing monitoring
• Prior experience in healthcare, managed care or insurance is required
• Expert-level Python: pandas, NumPy, scikit-learn, and deep learning frameworks (PyTorch and/or TensorFlow)
• Strong command of classical ML methods — gradient boosting (XGBoost, LightGBM), regularized regression, survival modeling, time-series analysis — as well as advanced approaches including HMMs, RNNs/LSTMs, causal inference, and reinforcement learning
• Hands-on experience with LLMs, RAG architectures, transformer models (BERT, GPT), and LLM fine-tuning, alignment, and evaluation workflows using Hugging Face, LangChain, and vector databases
• NLP proficiency across spaCy, scispaCy, MedSpaCy, and speech ML tools including ASR models, speaker diarization, and sentiment and emotion detection
• Proficiency with Databricks (Delta Lake, MLflow, Spark), AWS (S3, SageMaker, Bedrock, Redshift, Athena), and containerized deployment (Docker, Kubernetes/EKS)
• Strong SQL skills with demonstrated ability to extract, manipulate, and analyze data across multiple complex systems — relational, NoSQL, and data lakehouse environments
• Familiarity with MLOps practices: experiment tracking, model registry, CI/CD, and drift monitoring
• Ability to communicate complex modeling outputs clearly to clinical, operational, and executive audiences
• Collaborative by nature; comfortable driving alignment across product, engineering, and clinical teams simultaneously
• Self-directed, intellectually curious, and able to move quickly from an ambiguous problem to a working prototype
• Demonstrated ability to research, evaluate, and adopt new technical tools and methodologies as the AI and data science landscape evolves
• Experience mentoring junior data scientists and demonstrated commitment to building team knowledge through documentation, reusable frameworks, and internal best practices
• Ph.D. a plus

Benefits

• Flexible schedules
• Remote-first culture
• A nationally recognized wellness program

Company Overview