Applied Data Scientist

Before Sterling can prove anything, the data must be trustworthy. That's where this role starts. Sterling is building its data science function from scratch: greenfield infrastructure, a clear first problem (prove material changes extend mat life), and scope that grows into fleet intelligence, sales analytics, and technical diligence. You'll own the evidence stack needed to move initiatives through stage gates and build the models that turn messy field data into decisions the board will see.

Essential Functions: Data Integrity & Reconciliation: Audit and reconcile production records against system data across mats, RFID, and field collection sources. Establish 100% verifiability - every data point traceable, every discrepancy resolved - and maintain the lineage and quality standards that make downstream analysis defensible.

Predictive Modeling: Build, tune, and maintain production-grade models - random forest, gradient boosting, regression, Monte Carlo - that quantify mat longevity, treatment efficacy, and the business value of material changes. Then iterate as new data arrives.

Evidence Stack Framework: Maintain the confidence scoring methodology and reporting standards that gate every initiative before phase review. Ensure every claim - for IP, marketing, purchasing, or customers - is backed by appropriately sized, appropriately interpreted data.

Data Pipelines & Infrastructure: Build repeatable pipelines from raw field, sensor, and RFID sources to clean, analysis-ready datasets. Operate independently and establish the documentation standards Sterling will scale on.

RFID & Fleet Analytics: Turn raw RFID read data into fleet utilization, loss detection, and performance intelligence. Identify coverage gaps and reconcile reads against the physical fleet.

Dashboards & Stakeholder Reporting: Partner with IT to build operational dashboards in PowerBI that non-technical stakeholders use. Translate model outputs into clear, honest communication for operators, executives, and customers.

Commercial & Financial Analytics: As the foundation proves out, expand into CRM and pipeline analytics, win-rate modeling, marketing attribution, margin analysis, and the business cases that justify initiatives and capital allocation.

M&A & Diligence Support: Build data cases for acquisition target evaluation - fleet quality, customer concentration, margin defensibility, and operational KPIs.

Analytics Function Building: Define the data science standards, model documentation, and analytical best practices Sterling will scale with. Build the data function needed to power growth.

Do you have experience in Statistics?, Do you have a Bachelor's degree?, * Bachelor's degree in Data Science, Statistics, Computer Science, Engineering, or similar quantitative discipline

• 3-10 years in data science, applied analytics, or ML engineering - ideally in a company with physical operations or industrial data
• Demonstrated experience building, tuning, and deploying random forest and Monte Carlo models from scratch
• Daily-driver Python (pandas, numpy, scipy, scikit-learn, statsmodels) and production-quality SQL against messy real-world schemas
• Independent experience building ETL pipelines from raw sources to analysis-ready tables
• Strong statistical reasoning: hypothesis testing, sample sizing, uncertainty quantification, and the judgment to distinguish directional signal from statistically significant conclusion
• Track record of iterating models as new data arrives - adding features, retraining, recalibrating, or swapping algorithms
• Experience building operational dashboards for non-technical stakeholders
• Proven ability to communicate model outputs to non-technical audiences
• Experience with messy, real-world datasets including sensor gaps, inconsistent labels, and small samples, * Experience in construction, logistics, manufacturing, or industrial settings
• Prior "one-person data science team" experience - built the pipeline, ran the model, presented the results without engineering support
• Familiarity with CRM data (MS, Salesforce, HubSpot) for pipeline analytics or win-rate modeling
• Financial modeling in code (NPV, DCF, business case) and ROI analysis
• Exposure to RFID or IoT sensor data and field collection realities

Pulled from the full job description

• Health insurance
• Paid time off
• Vision insurance
• Dental insurance
• Life insurance
• Paid holidays, * Health Insurance: Medical, Dental, Vision
• Spending Accounts: H.S.A. and F.S.A. options.
• Life Insurance: $25k Employer Benefit
• Voluntary Benefits: Life, Disability, Pet
• Paid Time Off: Holidays, Vacation, Personal, Parental
• 401k with 3% Employer Contribution

Salary Range: $90 ,000-$115,000 dependent upon experience, and qualifications.

Sterling Site Access Solutions, LLC is a national vertically integrated site access service provider to the North American Energy Infrastructure Marketplace. An Illinois based company, with facilities located across the country, including regional construction offices, distribution centers and world class manufacturing facilities. As a data driven company, Sterling provides complete turnkey ground protection products and services to the transmission & distribution, renewables, civil and oil & gas markets. Sterling is committed to developing our employees, providing best in class customer service and being responsible stewards of the environment.