Senior Data Scientist
Enable Data Incorporated · Remote, Minnesota, United States
About The Role
- Immediate Full-Time Position for Senior Data Scientist (This will be FTE only)
- Design, develop, and deploy end-to-end AI/ML solutions for predictive modeling, risk stratification, behavioral analytics, and treatment pathway prediction.
- Build advanced NLP, LLM, and RAG-based applications, including prompt engineering, fine-tuning, and clinical AI guardrails for extracting insights from unstructured data.
- Develop speech AI and conversational AI capabilities using ASR, sentiment analysis, intent classification, Text-to-SQL, and decision-support models to improve care outcomes.
- Measure model performance through A/B testing, cohort analysis, explainability (SHAP/LIME), drift monitoring, and HIPAA-compliant governance.
- Collaborate with Data Engineering to implement feature stores, MLflow, MLOps, CI/CD pipelines, and scalable real-time/batch inference solutions.
- Mentor junior data scientists, evaluate emerging AI technologies, and drive the AI roadmap by translating advanced models into actionable healthcare solutions.
Requirements
7+ years of hands-on data science experience building and deploying predictive analytics, NLP, and Generative AI solutions in production, preferably within healthcare or other regulated industries.
Proven expertise across the entire machine learning lifecycle , including feature engineering, model development, deployment, monitoring, and optimization.
Advanced Python skills with pandas, NumPy, scikit-learn, PyTorch, and TensorFlow , along with classical ML techniques such as XGBoost, LightGBM, survival analysis, time-series forecasting, and deep learning.
Strong experience with LLMs, RAG architectures, transformer models (BERT/GPT), Hugging Face, LangChain, vector databases, and LLM fine-tuning and evaluation .
Hands-on expertise with NLP , speech AI, Databricks (Delta Lake, MLflow, Spark) , AWS (S3, SageMaker, Bedrock, Redshift, Athena) , SQL, Docker, Kubernetes, and MLOps practices.
Demonstrated leadership through mentoring, cross-functional collaboration, and the ability to communicate complex AI/ML insights to technical and business stakeholders while driving innovation and best practices.
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