Data & Analytics (D&A) Developer

Axelon Services Corporation; Greenville (SC); Hybrid; USD 90,000 - 120,000; Full time

Job summary

Axelon Services Corporation in Greenville, SC is seeking a data-focused professional to analyze quality data from multiple enterprise systems, build ML models for forecasting, and translate complex findings into actionable insights. The role requires collaboration with Program Managers, Operations, and Data Engineers on data assets, dashboards, and pipelines.

This is a hybrid, 12-month engagement, split across on-site and remote work.

Qualifications

• 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.

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.

Skills

Python

SQL

Data analysis

ML development

Statistical modeling

Data exploration

Data integration

Forecasting

LLMs basics

Multi-model comparison

Tools

TensorFlow

PyTorch

scikit-learn

Job description

• 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 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.