Data & Analytics (D&A) Developer II

We are seeking a curious, analytically sharp, and digitally passionate Data Scientist to join the Operations & Strategy team. This is an opportunity to create meaningful impact through data intelligence, advanced analytics, and AI-powered solutions.

As a core member of the team, you will act as a critical bridge between Engineering, business planning, operations, and IT teams. You will define data requirements, determine how data should be structured and utilized, and identify AI/ML solutions that deliver measurable business value.

The role will support centralized business operations and program reporting by providing harmonized insights, predictive analysis, and scenario-based outcomes to stakeholders. You will develop scenario-planning models, analyze project execution data, identify gaps between planned and actual performance, and support proactive, data-driven decision-making., Data Analysis & Intelligence

• Analyze data from multiple enterprise systems to identify trends, gaps, risks, and opportunities for improvement.
• Partner with Program Managers and Operations leaders to define data requirements and support business use cases.
• Transform large structured and unstructured datasets into actionable insights.
• Perform data-quality assessments and identify and resolve data defects and anomalies.

AI/ML Development

• Develop and validate machine-learning models supporting demand forecasting, scenario planning, and predictive analytics.
• Document analytical findings, model performance, assumptions, and data definitions.
• Collaborate with Data Engineers to implement data requirements and maintain Python-based pipelines for ETL, model training, and automated forecasting.
• Translate business and technical challenges into clear data-science and AI/ML problem statements.
• Use LLMs and prompt engineering to develop intelligent tools that improve decision-making and automate workflows.

Scenario Planning & Project Analytics

• Develop scenario models to evaluate demand forecasts, resource capacity, cost projections, and other business assumptions.
• Track project execution data across Primavera P6 and other project-management systems.
• Perform variance analysis between planned and actual performance, including budgets, timelines, resource utilization, and forecasted effort.
• Develop automated solutions to monitor assumptions and project performance throughout the project lifecycle.
• Support executive dashboards highlighting performance trends, risks, and projects requiring attention.

Data Ecosystem Optimization

• Review existing dashboards, analytical models, reports, and data pipelines.
• Interpret SQL queries, semantic models, and embedded business logic.
• Support the maintenance and enhancement of existing data solutions.
• Identify opportunities to improve, optimize, or consolidate reporting and analytics assets.
• Maintain alignment with established data standards and best practices.

Stakeholder Collaboration

• Translate complex data findings into clear and actionable insights for technical and non-technical stakeholders.
• Address internal user questions related to data, analytics, models, and reporting.
• Support centralized KPI reporting and enterprise-wide analytics initiatives.

Innovation & Continuous Improvement

• Collaborate with Data Analysts, Data Engineers, and cross-functional teams.
• Maintain a strong understanding of semantic data models to support consistent data interpretation.
• Stay current with developments in AI, machine learning, and data science.
• Recommend innovative approaches to improve data quality, analytics, modeling, and automation.

• Strong proficiency in Python for data analysis, statistical modeling, and machine learning development, including pandas, NumPy, scikit-learn, SciPy, curve fitting, and object-oriented programming.
• Experience developing scenario-planning and what-if analysis models.
• Foundational to intermediate knowledge of machine learning frameworks and methodologies, such as scikit-learn, XGBoost, or similar tools.
• Understanding of model-validation metrics, including R , MAE, RMSE, cross-validation, and custom scoring functions.
• Strong SQL skills, including querying, table joins, data manipulation, and interpretation of complex queries.
• Knowledge of statistical modeling, hypothesis testing, and experimental design.

Data Management

• Ability to independently explore enterprise datasets and identify patterns, gaps, and improvement opportunities.
• Experience cleaning, validating, and standardizing data across multiple systems.
• Experience integrating datasets from ERP, CRM, cloud, and enterprise platforms.
• Strong analytical ability to identify anomalies, outliers, errors, and unusual data patterns.

AI & Advanced Analytics

• Understanding of semantic data models and data modeling across diverse systems.
• Experience with forecasting, predictive analytics, and scenario modeling.
• Familiarity with Large Language Models (LLMs) and basic prompt-engineering techniques for business applications.

Dashboard & Data Logic

• Ability to review existing dashboards, reports, ML models, and analytical solutions to understand business requirements, design patterns, and underlying data sources.
• Strong capability to interpret complex SQL queries, data flows, and business logic.
• Experience tracing data lineage and understanding underlying data structures.
• Experience collaborating with Data Engineers to implement data requirements across pipelines and infrastructure.

Preferred Skills

• Experience with TensorFlow, PyTorch, neural networks, or deep-learning applications.
• Experience with pytest or similar unit-testing frameworks.
• Knowledge of Primavera P6, Microsoft Project, or similar project-management tools.
• Familiarity with MLOps, model versioning, experiment tracking, MLflow, or Weights & Biases.
• Exposure to Azure, AWS, or Google Cloud Platform data-science environments.
• Experience with advanced LLM applications, including fine-tuning, RAG, or agent frameworks.
• Understanding of data governance and responsible AI practices.
• Experience working with enterprise systems such as SAP, Salesforce, Databricks, or similar platforms., * Strong stakeholder-management and communication skills.
• Ability to explain complex technical concepts and AI/ML findings in clear business terms.
• Strong listening and requirements-gathering abilities.
• Professional, proactive, and solution-oriented communication style.
• Fluent English communication skills; additional languages are a plus.
• Strong analytical thinking, problem-solving, logical reasoning, and attention to detail.
• Technical curiosity and the ability to understand existing models, data pipelines, and analytical solutions.
• Ability to work effectively across cross-functional, international, and multicultural teams.
• Strong learning agility and willingness to adopt new technologies.
• Ownership, accountability, and proactive communication regarding deliverables, risks, dependencies, and escalations.