The Data Science Lead is a visionary architect of advanced analytical strategies, responsible for designing and validating complex AI/Machine Learning pipelines. This role ensures that all models are explainable, ethically sound, and aligned with client expectations and organizational objectives. The Data Science Lead provides technical leadership, mentors a team of data scientists and AI engineers, and oversees the entire lifecycle of predictive and prescriptive models to drive data-driven innovation and insights.
Responsibilities:
- Design, develop, and validate sophisticated forecasting, risk scoring, and anomaly detection models using advanced statistical and machine learning techniques
- Provide expert supervision and guidance on causal inference methods and intricate machine learning feature engineering processes
- Strategically manage the development and deployment of predictive modeling solutions across diverse and large-scale datasets
- Lead and mentor a high-performing team of AI engineers and data scientists, fostering a collaborative and innovative environment
- Oversee comprehensive model governance frameworks, ensuring explainability, audit trail compliance, and adherence to ethical AI principles
- Architect and optimize AI/ML pipelines within cloud-based analytical environments (e.g., Databricks, Snowflake)
- Collaborate with business intelligence developers to integrate model outputs into actionable dashboards and reports
- Drive continuous research and adoption of emerging AI/ML techniques and technologies relevant to the healthcare domain
- Communicate complex data science concepts and model insights effectively to both technical and non-technical stakeholders
- Ensure the reproducibility, scalability, and performance of all developed analytical solutions
Experience Required:
- 8+ years of progressive experience leading healthcare or public sector AI/Machine Learning teams and projects
- Extensive hands-on experience in designing, building, and deploying advanced predictive and analytical models
- Proven track record of managing complex data science initiatives from concept to production
- Deep understanding of explainable AI (XAI) principles and methodologies
Certifications / Education:
- Master’s degree (MS) in Data Science, Computer Science, Statistics, Applied Mathematics, or an equivalent quantitative field
- Machine Learning/Artificial Intelligence Certifications (preferred)
Skills:
- Explainable AI (XAI)
- Causal Inference
- Python/R (advanced proficiency)
- Databricks, Snowflake
- Machine Learning Algorithms
- Deep Learning
- Model Governance
- Team Leadership
- Data Architecture
- Problem-Solving
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