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

Stralynn Consulting Services, Inc

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Data Science Lead