Data Science Lead

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