Data Scientist

The Data Scientist will play a critical role in building, deploying, and scaling data science and AI solutions across Worldpac’s core business operations. This role blends hands-on technical execution with a sharp focus on business impact — deploying production-grade models that support pricing, procurement, supply chain, sales, and customer engagement. Ideal candidates will bring a strong foundation in traditional ML and optimization, experience deploying models in enterprise environments, and an understanding of how to apply emerging AI tools (including generative AI) for real-world automation and workflow augmentation.

Key Responsibilities:

Machine Learning:

· Develop, deploy, and scale machine learning and optimization models across thousands of SKUs and customer accounts to drive margin growth, reduce waste, and optimize procurement.

· Apply forecasting, segmentation, and prescriptive modeling to enhance sales intelligence, pricing strategies, and inventory decisions.

· Build and maintain production pipelines that integrate AI models into operational systems and business workflows.

· Implement best practices for versioning, deployment, and monitoring of models in production.

· Partner with data engineering and IT teams to ensure scalable and stable integration with enterprise systems.

Applied GenAI and Workflow Automation:

· Design and deploy GenAI-powered tools for use cases such as content automation for sales, intelligent customer interactions, and workflow augmentation — with a focus on measurable efficiency gains.

· Integrate generative AI capabilities into legacy systems and CRMs to enhance user productivity and decision-making.

Business Alignment and Impact:

· Collaborate with cross-functional stakeholders to identify and prioritize high-impact use cases.

· Use external data sources (e.g., D&B, Google Places, business registries) to enrich models and expand customer intelligence.

· Track the business value of deployed solutions — including cost savings, revenue growth, and operational efficiencies.

Team Collaboration and Mentorship:

· Contribute to a culture of continuous improvement, peer learning, and delivery excellence.

· Mentor junior team members in practical data science and production deployment strategies.

Qualifications:

· Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, or related field.

· 4+ years of experience in applied data science, with a track record of deploying models into production at enterprise scale.

· Strong foundation in classical Machine learning (e.g., regression, classification, clustering, time series) and optimization (e.g., linear/mixed-integer programming).

· Demonstrated ability to scale models across complex domains — millions of records, thousands of suppliers/customers/SKUs.

· Experience integrating AI outputs into ERP, CRM, or operational systems.

· Familiarity with practical applications of generative AI for business productivity.

· Proficient in Python and ML libraries (scikit-learn, XGBoost, pandas, etc.).

· Strong communication skills with the ability to explain technical topics to business stakeholders.

· Experience in B2B distribution, industrial supply, or adjacent sectors preferred

· Familiarity with business intelligence platforms (e.g., Power BI, Tableau) and integrating AI-driven insights into dashboards is a plus.

· Preferred candidates will also bring previous exposure to third-party data enrichment (e.g., D&B, business intelligence APIs) and/or understanding of cloud environments (AWS, Azure, GCP) and MLOps tooling (e.g., MLflow, Airflow).