Description

You will analyze enterprise data to drive improvements in demand forecasting, scenario modeling, and operational efficiency.

This role is hybrid.

Responsibilities

• Analyze quality data from SAP, Salesforce, Databricks, and finance systems to identify patterns and defects in datasets exceeding 100k rows.
• Develop and validate ML models for demand forecasting and predictive use cases using Python, TensorFlow, and PyTorch.
• Build and maintain Python-based ETL pipelines for model training and automated forecasting workflows in collaboration with Data Engineers.
• Leverage LLMs and prompt engineering to create intelligent tools that augment decision-making and automate workflows.
• Execute scenario planning to evaluate "what-if" outcomes, tracking project execution data in P6 to analyze variance between planned assumptions and actual performance.

Required Skills

• 5+ years of experience in data science or analytics.
• Strong proficiency in Python for data analysis, pipeline development, and ML model implementation.
• Experience with ML frameworks such as TensorFlow or PyTorch.
• Proficiency in SQL and working with large-scale structured and unstructured datasets.
• Hands-on experience with cloud platforms including Azure, AWS, or GCP.
• Familiarity with enterprise systems like SAP, Salesforce, and ERP/CRM environments.
• Ability to translate business requirements into technical data science problem statements.

Preferred Skills

• Experience with Power BI for building executive dashboards and visualizing data insights.
• Knowledge of semantic data models and Databricks ecosystems.
• Experience with project management tracking tools such as Primavera P6.

Key Skills

Education

Bachelor's degree