Data Scientist II

Rosendin•Anaheim, CA

6d•Onsite

About The Position

Rosendin is seeking a driven and analytical Data Scientist to join our Data Analytics Team. In this role, you will leverage advanced analytics, statistical modeling, machine learning, and data visualization to transform raw procurement, supply chain, and job-cost data into actionable strategic insights. The successful candidate will work closely with procurement leaders, category managers, estimators, and field operations to optimize spend, mitigate market risk, forecast electrical commodity price volatility, and enhance supplier performance across electrical construction projects nationwide.

Requirements

Bachelor’s or master’s degree in data science, Business Analytics, Statistics, Industrial Engineering, Supply Chain Management, or a related quantitative field.
Minimum of 2 years in procurement or related field.
3–5+ years of experience in data science, advanced analytics, or business intelligence—preferably within construction, manufacturing, engineering, or heavy industrial supply chains.
Proficiency in Python or R for data manipulation, statistical modeling, and machine learning.
Proficient in using a computer and Microsoft Office (Outlook, Word, Excel, etc.); Oracle preferred.
Advanced SQL skills for querying, joining, and structuring large enterprise datasets from relational databases and ERP platforms (e.g., Oracle, SAP).
Expertise with Power BI , Tableau , or similar enterprise BI tools.
Knowledgeable with time-series forecasting (ARIMA, Prophet, XGBoost), regression analysis, clustering, and NLP for spend categorizations.
Proficiency in advanced Microsoft Excel (complex functions, data modeling) and data pipeline orchestration.
Strong communication skills with the ability to convey technical analytical concepts to non-technical leaders and procurement teams.
Curiosity, self-direction, and a problem-solving mindset suited for building processes from concept to execution.

Nice To Haves

Preferably a master’s degree.

Responsibilities

Build, maintain, and enhance enterprise spend intelligence models across electrical commodities (e.g., conduit/EMT, wire & cable, switchgear, solar/renewables, heavy equipment rentals).
Develop and maintain statistical forecasting models to project monthly, quarterly, and annual procurement spend across major electrical material categories and equipment.
Perform advanced spend classification, taxonomy alignment, and anomaly detection across ERP, invoicing, and purchase order datasets.
Identify cost-saving opportunities, leakage, rebate capture, and volume aggregation points across regional organizational units.
Develop predictive price models and index tracking algorithms for volatile electrical raw materials (e.g., copper, aluminum, steel, resins) to inform risk mitigation strategies and bid estimating.
Analyze tariff impacts, market surcharges, and macroeconomic trends to project material lead times and cost exposure.
Design and deploy automated supplier scorecards evaluating vendor performance on lead-time compliance, pricing variance, fill rates, and quality standards.
Model supplier financial health, capacity constraints, and risk profiles to support strategic sourcing decisions and RFP evaluations.
Analyze transactional procurement workflows (requisitions, PO conversions, invoice matching, catalog compliance) to identify operational bottlenecks and cycle-time reduction opportunities.
Partner with IT and systems teams to design, test, and integrate machine learning algorithms into enterprise procurement tools (e.g., Oracle Fusion, automated requisition tools).
Track and model inventory recirculations, regional warehouse pilot metrics, and excess material usage to minimize waste across field operations.
Design, build, and maintain interactive dashboards and reports (Power BI / Tableau / OBTI) for Procurement leadership team, executive leadership and category managers.
Build predictive TCO and asset depreciation models across owned and leased vehicles/heavy equipment to determine optimal economic replacement thresholds (e.g., mileage, engine hours, maintenance cost tipping points).
Translate complex statistical findings into clear executive summaries, strategic recommendations, and actionable field guidance.
Serve as the primary analytics subject matter expert within the Procurement organization, promoting data-driven decision-making across the enterprise.