Data Scientist

Data Scientist

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 (R², 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 technical and non-technical audiences.
• Resolve customer and internal user queries related to model outputs, data insights, or data defects.
• Support the Operations team in delivering centralized data analysis-based reporting solutions (including KPI), providing harmonized insights and KPIs to business stakeholders across global business lines.
• Collaborate closely with cross-functional Data analysts and Data engineers to ensure data requirements are correctly understood and implemented at pipeline and infrastructure level.
• Build and maintain a deep understanding of Semantic Data Models to ensure consistent data interpretation across applications and business systems.
• Stay current with the latest advancements in AI, ML, and data science, proactively proposing new approaches that could enhance our solutions.
• Contribute to the evolution of Engineering Data Quality, bringing innovative ideas and a forward-thinking mindset to continuously improve our modeling and tooling landscape.

Nice to Have:

• Experience with TensorFlow, PyTorch, neural networks, or deep learning applications.
• Experience with pytest or similar frameworks for data science code quality.
• Experience with P6 (Primavera), MS Project, or similar project execution systems.
• Model versioning, experiment tracking (MLflow, Weights & Biases), deployment basics.
• Familiarity with Azure, AWS, or GCP for data science workflows.
• Experience with fine-tuning, RAG (Retrieval-Augmented Generation), or agent frameworks.
• Understanding of data governance principles and responsible AI practices.
• First-hand experience with SAP, Salesforce, Databricks, or similar ERP/CRM systems from a data consumption perspective.

Skills:

• Strong verbal and written communication skills.
• Excellent communication and presentation skills.
• Ability to communicate effectively with stakeholders.
• Analytical thinking with strong problem-solving abilities.
• Technical curiosity and willingness to learn new tools and techniques.
• Collaborative mindset and ability to work in dynamic environments.
• Self-motivated with a strong sense of accountability.
• Proactive communication style.