What are the responsibilities and job description for the Data & Analytics (D&A) Developer II position at Select Source International?
Job Title : 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
Job Description -
Required Technical Skills
Core Data Science & ML Tools
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 (R , 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
Location : 300 Garlington Road, Greenville, South Carolina, United States of America, 29615-4614
Duration: 12 months on W2 with possible extensions
Job Description -
Required Technical Skills
Core Data Science & ML Tools
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 (R , 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