Lead Data Scientist - Healthcare

Key Responsibilities

• Lead the design, development, and enhancement of forecasting models to improve prediction accuracy and business outcomes.
• Build and deploy advanced Deep Learning models for large-scale structured and unstructured datasets.
• Develop Generative AI / Explainability models to provide transparent and interpretable insights from predictive models.
• Analyze healthcare datasets including claims, patient, provider, operational, or clinical data.
• Collaborate with business stakeholders, product teams, and engineering teams to translate business challenges into scalable AI solutions.
• Monitor model performance, retrain models, and optimize algorithms for production environments.
• Mentor junior data scientists and provide technical leadership across the project.
• Ensure compliance with healthcare data privacy and governance standards.

Requirements

• 8-10 years of experience in Data Science / Machine Learning roles.
• Strong hands-on expertise in Forecasting models (time series, demand forecasting, predictive analytics).
• Experience with Deep Learning frameworks such as TensorFlow, PyTorch, or Keras.
• Proven experience building Generative AI / Explainability models using LLMs, SHAP, LIME, or similar frameworks.
• Mandatory experience working in the Healthcare domain.
• Strong programming skills in Python, SQL, and ML libraries.
• Experience with cloud platforms such as AWS, Azure, or GCP preferred.
• Excellent stakeholder communication and leadership skills.

Benefits

This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.

Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.