Data & Analytics (D&A) Developer II

Location: 300 Garlington Road, Greenville, South Carolina, United States of America, 29615-4614

Duration: 12+ months on W2 with possible extensions

Required Technical Skills

• Python: Strong proficiency in data analysis, statistical modeling, and ML development (pandas, numpy, scikit-learn, scipy, curve fitting, object-oriented programming)
• Scenario Planning & What-If Analysis: Ability to build multi-scenario models to test assumptions and evaluate alternative planning outcomes
• Machine Learning: Foundational to intermediate experience with ML frameworks and methodologies (scikit-learn, XGBoost, or similar)
• Model Evaluation: Understanding of model validation metrics (R², MAE, RMSE, cross-validation, custom scoring functions)
• SQL: Proficiency in querying, joining tables, data manipulation, and interpreting complex queries
• Statistical Analysis: Understanding of statistical modeling, hypothesis testing, and experimental design

Data Management Competencies

• Data Exploration: Ability to independently explore enterprise datasets and identify patterns, gaps, and opportunities
• Data Cleaning: Experience handling messy data, identifying inconsistencies, and standardizing formats across heterogeneous systems
• Data Integration: Experience merging multiple datasets from various enterprise data sources (SAP, Salesforce, Databricks, ERP/CRM)
• Anomaly Detection: Sharp eye for finding outliers, errors, and unusual patterns in structured and unstructured data

AI & Advanced Analytics

• Semantic Data Models: Understanding of data modeling concepts across heterogeneous systems
• Forecasting & Prediction: Experience developing models for scenario modeling and predictive use cases
• Large Language Models (LLMs): Familiarity with LLMs and basic prompt engineering techniques for practical business applications

Dashboard & Logic Comprehension

• Reverse Engineering: Ability to review existing dashboards, ML models, and reports to understand design patterns, business requirements, and underlying data sources
• SQL Query Analysis: Strong capability to read and interpret complex SQL queries to understand data flows and business logic
• Data Source Understanding: Skills to trace data lineage, review prepared data sources, and comprehend underlying data structures
• Pipeline Collaboration: Experience working with Data Engineers to ensure data requirements are correctly implemented at pipeline and infrastructure level