Traditional Data Scientist ERP Conversion Projects

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Traditional Data Scientist ERP Conversion Projects

Remote

12+ years

Summary:

We are seeking a traditional data scientist with strong machine learning capabilities to support ERP (Enterprise Resource Planning) system conversion initiatives. This role will focus on data quality analysis, forecasting, migration validation, and anomaly detection using structured enterprise datasets.

Key Responsibilities:

• Collaborate with ERP architects and functional teams to analyze legacy data structures and develop transformation logic.
• Apply machine learning techniques to detect anomalies in financial, supply chain, and operational data pre/post migration.
• Build regression or classification models to forecast system impacts and user adoption patterns post-conversion.
• Validate data integrity using statistical profiling, outlier detection, and reconciliation logic.
• Partner with business users to define KPIs and build dashboards for monitoring conversion success.
• Support data cleansing, enrichment, and validation during cutover and parallel testing phases.

Required Skills:

• 5+ years in data science or advanced analytics roles in enterprise environments.
• Strong understanding of ERP systems (e.g., SAP, Oracle, NetSuite) data domains: finance, procurement, inventory, HR, etc.
• Proficient in Python (pandas, scikit-learn) or R for statistical analysis and machine learning.
• Hands-on experience with SQL and data wrangling for large-scale datasets.
• Ability to build models for forecasting, clustering, and anomaly detection.
• Experience with data visualization tools (e.g., Power BI, Tableau) to communicate insights.
• Familiarity with data governance, migration, and ETL concepts.

Preferred Experience:

• Prior involvement in ERP migrations
• Experience with data quality frameworks and tools (e.g., Great Expectations, Talend).
• Exposure to cloud platforms like Google Cloud Platform, AWS, or Azure for data storage and processing.