Data & Analytics Developer
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Job description
The Data & Analytics (D&A) Developer II / Data Scientist supports the HDPE Operations & Strategy team, serving as the bridge between engineering domain knowledge, business operations, and IT execution. This role defines data requirements, builds AI/ML and scenario-planning models, and delivers harmonized insights and reporting to business stakeholders worldwide. Responsibilities: Analyze data across enterprise systems (SAP, Salesforce, Databricks, Power BI); develop and validate machine learning models for forecasting and scenario planning; build and maintain Python-based data pipelines; track project execution through P6 and other project systems; reverse-engineer existing dashboards and SQL logic; and translate technical findings into actionable business insights. RESPONSIBILITIES Data Analysis & Intelligence
- Analyze data from multiple enterprise systems (SAP, Salesforce, Databricks, Power BI, labor and finance systems) to identify patterns, gaps, and improvement opportunities
- Work with Program Managers and Operations leaders to define relevant data assets and specify how data should be accessed, interpreted, and used
- Transform large structured/unstructured datasets (100k+ rows) into actionable insights
- Conduct data quality checks and resolve data defects across enterprise platforms
AI/ML Model Development & Deployment
- Develop and validate machine learning models supporting demand forecasting and scenario modeling
- Document analytical findings, model performance, and data definitions for transparency and reproducibility
- Build and maintain Python-based data pipelines for ETL, model training, and automated forecasting workflows in collaboration with Data Engineers
- Translate business data challenges into concrete data science and AI/ML problem statements
- Leverage LLMs and prompt engineering to build tools that augment decision-making and automate workflows
Scenario Planning & Project Execution Analytics
- Design and execute scenario planning models to test business assumptions and evaluate what-if outcomes
- Track project execution data across P6 (Primavera) and other systems, linking planning assumptions to actual performance
- Support variance analysis between planned assumptions and actual execution to identify gaps and trends
- Build automated tracking solutions monitoring assumption validity through project lifecycle stages
- Collaborate with Program Managers to refine planning assumptions based on execution learnings
- Provide data pipelines and data to build executive dashboards visualizing assumption-to-execution alignment
Existing Data Ecosystem & Optimization
- Review existing dashboards, models, and data pipelines to understand design patterns and data flows
- Read and interpret SQL queries and business logic embedded in current reports and analytical systems
- Identify opportunities to optimize or consolidate existing reporting and modeling assets
- Maintain consistency with established data standards and best practices
Business Stakeholder Collaboration & Continuous Improvement
- Translate complex data findings and model outputs into clear, actionable insights for technical and non-technical audiences
- Support centralized, KPI-based reporting solutions for business stakeholders across global business lines
- Collaborate with Data Analysts and Data Engineers to ensure data requirements are implemented correctly at the pipeline and infrastructure level
- Stay current with AI, ML, and data science advancements, proposing new approaches to enhance solutions
Requirements
- Bachelor's degree in Data Science, Computer Science, Engineering, or related field.
- Strong proficiency in Python (pandas, numpy, scikit-learn, scipy) and SQL.
- Experience with statistical modeling, scenario/what-if analysis, and model validation.
- Familiarity with enterprise data systems (SAP, Salesforce, Databricks) and LLM/prompt engineering concepts.
- Strong communication skills and ability to work across international, multicultural teams.