Data Scientist

SUMMARY

The Data Scientist is a high-impact, strategic role responsible for identifying, developing, and deploying data-driven solutions that transform how Hercules Industries operates. This position goes beyond traditional analytics, focusing on driving measurable business value through advanced modeling, insight generation, and business integration.

Salary: $98,000 - $113,000 per year, DOE.

The Data Scientist partners with leadership across supply chain, operations, and commercial functions to uncover opportunities, challenge legacy processes, and enable a step-change in decision‑making and performance. This role contributes to both incremental optimization and breakthrough innovation that support long‑term enterprise value creation.

Drive enterprise performance by translating complex business challenges into data‑driven solutions that improve decision‑making, optimize operations, and enable scalable, sustainable competitive advantage.

This role contributes to outcomes across forecasting → inventory → operations → performance by embedding analytics into core business processes and building a foundation for a modern, data‑driven organization.

• Partner with executive and functional leaders to identify high‑impact opportunities for data‑driven transformation.
• Translate ambiguous business challenges into structured analytical problem statements.
• Quantify potential business value (inventory, service, margin, cash flow).
• Prioritize initiatives based on strategic importance and return.

• Consistently identify high‑value opportunities aligned with business priorities.
• Demonstrate strong problem‑structuring and critical thinking skills.
• Connect analytical work directly to financial and operational outcomes.

• Design, build, and deploy predictive and prescriptive models, including: demand forecasting, inventory optimization (safety stock, EOQ/MOQ, multi‑-echelon), supplier performance and lead‑time variability, operational efficiency and throughput optimization, pricing segmentation and optimization, estimating tools and economic optimization models.
• Apply statistical, machine‑learning, and optimization techniques.
• Ensure models are scalable, interpretable, and aligned with business realities.

• Develop models that are both technically sound and practically applicable.
• Balance sophistication with usability and business adoption.
• Continuously improve model performance and relevance.

• Perform exploratory data analysis to identify trends, anomalies, and root causes.
• Partner with Data Engineering to ensure clean, reliable datasets.
• Identify and address data gaps and integrity issues.
• Build datasets and structures for ongoing decision‑making.

• Produce actionable insights, not just analysis.
• Improve data quality and usability over time.
• Build reusable data assets that scale across the organization.

• Translate analytical outputs into clear, actionable insights.
• Embed models into business processes (SIOP, purchasing, planning, inventory).
• Drive adoption through usability and alignment with workflows.
• Support leadership decision‑making with data‑driven recommendations.

• Drive adoption of analytics into daily operations.
• Communicate insights clearly to non‑technical stakeholders.
• Ensure solutions are practical and sustainable.

• Challenge legacy processes and identify step‑change improvements.
• Lead or support initiatives related to forecasting, inventory, service, and digital enablement.
• Contribute to building a data‑driven culture.

• Actively contribute to transformation initiatives.
• Identify opportunities for innovation and disruption.
• Build momentum for continuous improvement and change.

• Present findings and recommendations to senior leadership.
• Communicate complex concepts in clear business terms.
• Influence cross‑functional stakeholders to adopt new approaches.

• Effective…