Sr. Data Scientist

Role: Sr Data Scientist

Location: Remote

Duration: Full time

Job description:

• 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.

Required Skills:

• 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.