Description

Key Responsibilities
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

 
Essential Soft Skills & Competencies
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

Key Skills

Education

Bachelor's degree