Lead Data Scientist

About the role

The Lead Data Scientist serves as a technical leader responsible for developing, deploying, and scaling advanced analytical, machine learning, and optimization solutions that drive measurable, profitable outcomes across Safelite’s Consumer Sales & Pricing activities.

Responsibilities

• Lead the development of advanced machine learning models, statistical frameworks, and optimization solutions to support consumer and sales growth.
• Define and enforce best practices for model development, validation, deployment, and monitoring.
• Drive innovation through the application of cutting-edge techniques, including: Deep learning, Natural language processing (NLP), Causal inference, Reinforcement learning and emerging AI technologies.
• Serve as the technical escalation point for complex analytical and modeling challenges.
• Continuously evaluate emerging methods and ensure their practical applicability to business problems.
• Own the full lifecycle of data science solutions: problem framing, feature engineering, model development, production deployment, ongoing monitoring and improvement.
• Translate ambiguous, high-level business questions into structured analytical approaches.
• Ensure models are scalable, performant, explainable, and maintainable in production environments.
• Partner with engineering and platform teams to operationalize models.
• Work closely with senior business, sales, and product stakeholders to identify high-value use cases. Translate complex model outputs into actionable insights and clear strategic recommendations.
• Quantify business impact and ensure alignment with organizational KPIs, revenue goals, and growth strategies.
• Influence decision-making through compelling, data-driven narratives, not just technical outputs.
• Mentor both junior data scientists on advanced analytical methodologies and software engineering and coding best practices.
• Contribute to building a high-performance analytics culture.
• Lead knowledge sharing through code reviews, technical standards, and design discussions.
• Collaborate with data engineering, platform, and architecture teams to define data requirements, pipelines, and scalable analytics architecture.
• Evaluate and integrate new tools, frameworks, and technologies into the analytics ecosystem where they deliver clear value.

Requirements

• Bachelor's Degree In Data Science, Statistics, Computer Science, Mathematics, Physics, Engineering, or a related quantitative field
• Master's Degree In Data Science, Statistics, Computer Science, Mathematics, Physics, Engineering, or a related quantitative field
• 7-9 years Experience in data science, machine learning, applied research, or advanced analytics
• Proficient in SQL
• Advanced experience with top statistical programming languages (R or Python)
• Experience working in cloud-based analytics environments
• Hands-on experience building Regression models, Classification models, Clustering models
• Strong understanding of machine learning algorithms, statistical modeling, and optimization techniques
• Experience with A/B, multi-arm, and pre-post testing
• Familiarity with ML operations (MLOps), including versioning, monitoring, and CI/CD pipelines
• Familiarity with GenAI/LLMs for price recommendation explainability; competitive intelligence from unstructured data
• Previous work on pricing strategy, pricing optimization, or pricing engines
• Experience designing experiments without commercial testing platforms
• Experience collaborating with data engineering and data management teams to deploy models in production, monitor model drift, and implement re-training cadences.