Lead Data Scientist

Safelite • Columbus, OH

4d • Remote

About The Position

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. The role owns end‑to‑end data science solutions—from problem framing through production deployment—while translating complex analytical outputs and AI innovation into actionable business insights. Safelite’s Consumer Sales & Pricing Team drives profitable growth by partnering with sales, product, and business leaders, to develop pricing and promotional recommendations that balance customer value with revenue goals. Their work plays a critical role in pricing optimization, experimentation strategy, and advancing the organization’s analytics capabilities through technical leadership, mentorship, and innovation.

Requirements

• Bachelor's Degree In Data Science, Statistics, Computer Science, Mathematics, Physics, Engineering, or a related quantitative field Required
• 7-9 years Experience in data science, machine learning, applied research, or advanced analytics Required
• 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.

Nice To Haves

• Master's Degree In Data Science, Statistics, Computer Science, Mathematics, Physics, Engineering, or a related quantitative field Preferred

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 problem.
• 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.
• Advocate for data quality, governance, and reproducibility and documentation standards.
• Evaluate and integrate new tools, frameworks, and technologies into the analytics ecosystem where they deliver clear value.
• Performs other duties as assigned.
• Complies with all policies and standards.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior