Data Scientist

Job Purpose

LP Building Solutions, a large specialty building products manufacturer, is looking for a full-time data scientist to join the data analytics team. Leveraging advanced analytical techniques, statistical modeling, and/or machine learning, you will partner with the business to uncover opportunities, optimize performance, and drive data-informed outcomes. This role will partner closely with commercial, operations, supply chain, corporate, and finance teams to identify opportunities, develop predictive and prescriptive models, and deliver actionable insights that improve revenue growth, operational efficiency, and margin performance.

This role combines advanced data science techniques with business partnership to identify opportunities, solve complex problems, and generate insightsfordecisionsupport.

The ideal candidate combines strong technical expertise in statistical modeling and advanced analytics with the ability to translate complex data into clear, business-relevant insights. This individual will work with large, complex datasets spanning manufacturing, distribution, pricing, and customer behavior. This role requires a strong blend of analytical rigor and business acumen, with the ability to work cross-functionally and influence stakeholders. While this role does not require hands-on data engineering responsibilities, it demands close collaboration with the Data Engineering team.

Candidates should have a solid understanding of core data engineering concepts to effectively partner, translate business needs, and ensure alignment across data workflows and infrastructure.

• Complete end-to-end data science initiatives, from business problem framing and data exploration through model development, validation, deployment partnership, and performance monitoring.
• Work directly with internal and external customers to define success criteria, hypotheses, and measurable outcomes. Translate the business needs into analytics/reporting requirements to support executive decisions and workflows with required information.
• Design, build, and evaluate predictive, prescriptive, and statistical models that improve decision‑making, operational efficiency, customer outcomes, or financial performance.
• Design and evaluate experiments to test hypotheses, measure impact, and guide decisions (e.g., A/B, Multivariate, simulation, scenario, Quasi, etc.).
• Apply advanced analytical methods such as machine learning, forecasting, optimization, causal inference, and experimentation to solve high value business problems.
• Proactively identify trends and patterns and generate insights for business units and senior leadership.
• Work with the IT Data Engineering team to integrate data from multiple sources including CRM, ERP, operational systems, web analytics, and third‑party datasets for analysis.
• Research and implement cutting‑edge techniques and tools in machine learning/artificial intelligence to make data analysis more efficient.
• Present insights and recommendations to stakeholders in a clear, business‑focused manner. You will need to simplify complex methodologies into actionable business insights.
• Establish processes and tools that monitor, analyze and continuously improve model performance and data accuracy.
• Partner with the analytics leadership team to align initiatives and strategy. Contribute to enterprise analytics roadmap and best practices.
• Support other analytics team members by providing technical guidance, peer review, and thought partnership.

• 5+ years of progressive experience in data science, advanced analytics, or a closely related field, preferably supporting marketing or commercial teams.
• Experience in the development of Machine Learning models and AI frameworks.
• Experience working with data visualization and business intelligence tools to communicate insights effectively (e.g., Tableau, Power BI, or similar tools).
• Experience working with enterprise data platforms (e.g., Snowflake, Databricks, Cloudera, Big Query).
• Experience working with data from large enterprise applications (e.g., ERP, CRM, or operational systems).
• Preferred…