Data & Analytics (D&A) Developer Job Greenville area,South Carolina USA,IT/Tech

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.

Summary

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…