Data Scientist

Job Overview:
Pay Range $45.96hr - $50.96hr Requirement/Must Have: 1+ years of experience in data analysis, statistical modeling, and ML development using Python (pandas, numpy, scikit-learn, scipy, curve fitting, object-oriented programming). Ability to build multi-scenario models to test assumptions and evaluate alternative planning outcomes. Foundational to intermediate experience with ML frameworks and methodologies (scikit-learn, XGBoost, or similar). Understanding of model validation metrics (R , MAE, RMSE, cross-validation, custom scoring functions).

Proficiency in SQL for querying, joining tables, data manipulation, and interpreting complex queries. Understanding of statistical modeling, hypothesis testing, and experimental design. Ability to independently explore enterprise datasets and identify patterns, gaps, and opportunities. Experience handling messy data, identifying inconsistencies, and standardizing formats across heterogeneous systems. Experience merging multiple datasets from various enterprise data sources (SAP, Salesforce, Databricks, ERP/CRM). Sharp eye for finding outliers, errors, and unusual patterns in structured and unstructured data.

Understanding of data modeling concepts across heterogeneous systems. Experience developing models for scenario modeling and predictive use cases. Familiarity with Large Language Models (LLMs) and basic prompt engineering techniques for practical business applications. Ability to review existing dashboards, ML models, and reports to understand design patterns, business requirements, and underlying data sources. Strong capability to read and interpret complex SQL queries to understand data flows and business logic.

Skills to trace data lineage, review prepared data sources, and comprehend underlying data structures. Experience working with Data Engineers to ensure data requirements are correctly implemented at pipeline and infrastructure level. Responsibilities:
Analyze quality data from multiple enterprise systems (SAP, Salesforce, Databricks, Power BI, labor systems, finance data) to identify patterns, gaps, and opportunities for data-driven improvements. Work with Program Managers and/or Operations leaders to define which data assets are relevant for business use cases and specify how data from different systems should be accessed, interpreted, and used. Transform structured/unstructured datasets (often 100k+ rows) into actionable insights.

Conduct data quality checks and identify/resolve data defects and abnormalities across enterprise platforms. Develop and validate Machine Learning models that support demand forecasting, scenario modeling, and predictive use cases for short-term and long-term business goals. Document analytical findings, model performance, and data definitions clearly to ensure transparency and reproducibility across the team. Experience working with Data Engineers to ensure data requirements are correctly implemented;

ability to build and maintain Python-based data pipelines for ETL, model training, and automated forecasting workflows. Translate business technical data challenges into concrete data science and AI/ML problem statements, acting as the domain-aware bridge between Engineering/Operations and the Digital team. Leverage Large Language Models (LLMs) and prompt engineering to build intelligent tools that augment human decision-making and automate workflows. Design and execute scenario planning models to test business assumptions (demand forecasts, resource capacity, cost projections) and evaluate 'what-if' outcomes for strategic decision-making.

Track project execution data across P6 (Primavera) and other project management systems, linking planning assumptions to actual execution performance. Support variance analysis between planned assumptions (forecast hours, budgets, timelines) and actual project execution data to identify gaps, root causes, and trends. Build automated tracking solutions that monitor assumption validity as projects progress through lifecycle stages (planning design execution closeout). Collaborate with Program Managers to refine planning assumptions based on execution learnings and historical pattern analysis.

Provide data pipeline and data to build executive dashboards that visualize assumption-to-execution alignment, highlighting projects at risk due to assumption breakdown. Review and analyze existing dashboards, models, and data pipelines to understand design patterns, business requirements, and data flows. Read and interpret SQL queries, business logic, and semantic models embedded in current reports and analytical systems. Understand underlying data structures and prepared data sources to support maintenance and enhancement.

Identify opportunities to optimize or consolidate existing reporting and modeling assets. Maintain consistency with established data standards and best practices. Translate complex data findings and model outputs into clear, actionable business insights for both…