Senior Data Scientist

Careforth•UNAVAILABLE, Massachusetts

21d•Remote

About The Position

The Senior Data Scientist will be a cornerstone of our AI/ML organization, spearheading the design and deployment of advanced predictive models, NLP solutions, and AI-powered tools that define how we understand and act on risk across our caregiver support platform. The ideal candidate brings the depth to work across the full data science stack — from classical machine learning and survival modeling to large language models and conversational AI — with a track record of taking models from prototype to production in regulated, data-sensitive environments. You will lead the development of intelligence that enables care teams, health plan partners, and program leaders to understand population health drivers, anticipate risk before it becomes crisis, and personalize interventions at scale — turning complex behavioral and clinical signals into decisions that improve lives.

Requirements

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
Experience mentoring junior data scientists and demonstrated commitment to building team knowledge through documentation, reusable frameworks, and internal best practices.
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.

Nice To Haves

Ph.D. a plus.

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.