Lead Data Scientist

Safelite•Columbus, OH

3d•Onsite

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. This position 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
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.

Nice To Haves

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

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.