Data & Analytics (D&A) Developer

Location: Greenville, SC
Work Mode: Hybrid
Duration: 12 Months

Responsibilities

• Analyze quality data from multiple enterprise systems to identify patterns, gaps, and opportunities for data-driven improvements.
• Collaborate with Program Managers and Operations leaders to define relevant data assets for business use cases.
• Transform structured and unstructured datasets into actionable insights.
• Conduct data quality checks and resolve data defects and abnormalities across enterprise platforms.
• Develop and validate Machine Learning models for demand forecasting, scenario modeling, and predictive use cases.
• Document analytical findings and model performance for transparency and reproducibility.
• Collaborate with Data Engineers to ensure data requirements are correctly implemented in pipelines and infrastructure.
• Design and execute scenario planning models to test business assumptions and evaluate "what-if" outcomes.
• Track project execution data across project management systems and support variance analysis.
• Provide data pipeline support to build executive dashboards that visualize assumption-to-execution alignment.
• Collaborate with Program Managers to refine planning assumptions based on execution learnings.
• Review and analyze existing dashboards, models, and data pipelines to understand design patterns and data flows.
• Translate complex data findings into clear, actionable business insights for both technical and non-technical audiences.
• Support the Operations team in delivering centralized data analysis-based reporting solutions.
• Collaborate closely with cross-functional teams to ensure data requirements are correctly understood and implemented.
• Stay current with the latest advancements in AI, ML, and data science.

Requirements

• Strong proficiency in Python for data analysis, statistical modeling, and ML development.
• Ability to build multi-scenario models for testing assumptions and evaluating planning outcomes.
• Foundational to intermediate experience with ML frameworks and methodologies.
• Proficiency in SQL for querying, joining tables, data manipulation, and interpreting complex queries.
• Understanding of statistical modeling, hypothesis testing, and experimental design.
• Experience in data exploration, cleaning, integration, and anomaly detection.
• Understanding of data modeling concepts and semantic data models.
• Experience developing forecasting and prediction models.
• Familiarity with Large Language Models (LLMs) and basic prompt engineering techniques.
• Ability to review existing dashboards, ML models, and reports to understand design patterns and business requirements.

Preferred Skills

• Experience with TensorFlow, PyTorch, neural networks, or deep learning applications.
• Familiarity with pytest or similar frameworks for data science code quality.
• Experience with project execution systems like P6 (Primavera) or MS Project.
• Knowledge of MLOps, model versioning, and experiment tracking.
• Familiarity with cloud platforms like Azure, AWS, or GCP for data science workflows.
• Experience with advanced LLM applications, fine-tuning, or agent frameworks.
• Understanding of data governance principles and responsible AI practices.
• Experience with enterprise systems like SAP, Salesforce, or Databricks.

Essential Soft Skills & Competencies

• Excellent stakeholder interaction skills and the ability to translate technical concepts into business value.
• Strong analytical thinking, problem-solving abilities, and attention to detail.
• Intellectually curious with a collaborative mindset and learning agility.
• Ability to operate in dynamic, evolving environments and work across international, multicultural teams.
• Proactive communication style with a solution-oriented approach.