Data Scientist #26-18742

• Client is accelerating the path to more reliable, affordable, and sustainable energy, while helping our customers power economies and deliver the electricity that is vital to health, safety, security, and improved quality of life.
• We are seeking a curious, analytically sharp, and digitally passionate Data Scientist to join our HDPE Operations & Strategy team - a team where collaboration and participative leadership are not just words, but the way we work every day. This is your opportunity to create real impact from day one. As a core member of our HDPE team, you will be at the forefront of our engineering vision - where data intelligence and AI-powered tools redefine how we manage, predict, and operate across Client’s global business.
• You will act as the critical bridge between our Engineering domain data knowledge, business planning, operations and our IT execution team - defining what data we need, how it should be structured and used, and what AI/ML solutions can unlock the most value. You will support centralized business operations and program reporting that delivers harmonized insights and predicted range of outcomes to business stakeholders worldwide.
• You will build scenario planning models that test critical business assumptions and track project execution through P6 and enterprise systems, identifying gaps between plan and reality to drive proactive decision-making. This role will be critical in efforts to optimize HDPE Operations program management activities, Data Analysis & Intelligence
• 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

AI/ML Model Development & Deployment

• 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
• Pipeline Collaboration & Development: 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

Scenario Planning & Project Execution Analytics

• 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

Existing Data Ecosystem & Optimization

• Review and analyze existing Client 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 Client data standards and best practices

Business Stakeholder Collaboration

• 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 Client’s global business lines

Innovation & Continuous Improvement

• 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

• Python: Strong proficiency in data analysis, statistical modeling, and ML development (pandas, numpy, scikit-learn, scipy, curve fitting, object-oriented programming)
• Scenario Planning & What-If Analysis: Ability to build multi-scenario models to test assumptions and evaluate alternative planning outcomes
• Machine Learning: Foundational to intermediate experience with ML frameworks and methodologies (scikit-learn, XGBoost, or similar)
• Model Evaluation: Understanding of model validation metrics (R², MAE, RMSE, cross-validation, custom scoring functions)
• SQL: Proficiency in querying, joining tables, data manipulation, and interpreting complex queries
• Statistical Analysis: Understanding of statistical modeling, hypothesis testing, and experimental design

Data Management Competencies

• Data Exploration: Ability to independently explore enterprise datasets and identify patterns, gaps, and opportunities
• Data Cleaning: Experience handling messy data, identifying inconsistencies, and standardizing formats across heterogeneous systems
• Data Integration: Experience merging multiple datasets from various enterprise data sources (SAP, Salesforce, Databricks, ERP/CRM)
• Anomaly Detection: Sharp eye for finding outliers, errors, and unusual patterns in structured and unstructured data

AI & Advanced Analytics

• Semantic Data Models: Understanding of data modeling concepts across heterogeneous systems
• Forecasting & Prediction: Experience developing models for scenario modeling and predictive use cases
• Large Language Models (LLMs): Familiarity with LLMs and basic prompt engineering techniques for practical business applications
• Dashboard & Logic Comprehension
• Reverse Engineering: Ability to review existing dashboards, ML models, and reports to understand design patterns, business requirements, and underlying data sources
• SQL Query Analysis: Strong capability to read and interpret complex SQL queries to understand data flows and business logic
• Data Source Understanding: Skills to trace data lineage, review prepared data sources, and comprehend underlying data structures
• Pipeline Collaboration: Experience working with Data Engineers to ensure data requirements are correctly implemented at pipeline and infrastructure level

Nice to Have Skills

• Advanced ML/Deep Learning: Experience with TensorFlow, PyTorch, neural networks, or deep learning applications
• Unit Testing: pytest or similar frameworks for data science code quality
• Experience with P6 (Primavera), MS Project, or similar project execution systems
• MLOps: Model versioning, experiment tracking (MLflow, Weights & Biases), deployment basics
• Cloud Platforms: Familiarity with Azure, AWS, or GCP for data science workflows
• Advanced LLM Applications: Experience with fine-tuning, RAG (Retrieval-Augmented Generation), or agent frameworks
• Data Governance: Understanding of data governance principles and responsible AI practices
• Enterprise Systems: First-hand experience with SAP, Salesforce, Databricks, or similar ERP/CRM systems from a data consumption perspective, Communication & Collaboration
• Stakeholder interaction skills: Ability to engage with non-technical audiences and translate complex technical concepts and AI/ML findings into business value
• Understanding & listening skills: Proven ability to grasp business requirements, ask clarifying questions, and define clear data requirements for distributed execution teams
• Positive communication style: Professional, proactive, and solution-oriented approach
• Multilingual capability: Fluent in English (written and spoken); additional languages are a plus

Mindset & Work Style

• Analytical thinking: Strong problem-solving abilities with attention to detail, logical reasoning, and scientific rigor
• Technical curiosity: Intellectually curious, able to dive into existing work, understand how ML models and data pipelines were built, and learn from established patterns
• Collaborative mindset: Comfortable operating in dynamic, evolving environments and working across international, multicultural teams and time zones
• Learning agility: Self-motivated to learn new tools, techniques, and business domains quickly; stay current with AI/ML advancements
• Accountability: Takes ownership of deliverables, escalates issues appropriately, and participates in daily, weekly, and monthly meeting rhythm with CLIENT
• Proactive communication: Communicate project status, risks, dependencies, and potential escalations early and clearly