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Tiger Analytics is looking for experienced Data Scientists to join our fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.

We are seeking a Data Scientist with strong downstream refining experience to drive data-driven insights across refinery operations, economics, and reliability. This role partners closely with process engineers, operations, planning, maintenance, and commercial teams to optimize refinery performance using advanced analytics, machine learning, and domain-informed modeling.

You'll work on high-impact problems such as yield optimization, energy efficiency, unit reliability, predictive maintenance, and margin improvement-turning complex refinery data into actionable intelligence.

Key Responsibilities

Analytics & Modeling

• Develop, validate, and deploy statistical, ML, and optimization models for refining operations
• Build models supporting:
• Unit performance optimization (e.g., CDU/VDU, hydrotreating, cracking)
• Energy efficiency and utilities optimization
• Yield and cut-point optimization
• Predictive maintenance and reliability analytics
• Fouling, corrosion, and anomaly detection
• Apply time-series analysis to high-frequency plant data (DCS, historian)

Refining Domain Collaboration

• Partner with process engineers, operations, maintenance, and planning teams to translate refinery problems into analytical solutions
• Incorporate first-principles knowledge (mass & energy balances, constraints, process limits) into data models
• Interpret model results in the context of refinery economics, safety, and operability

Communication & Impact

• Clearly communicate insights to technical and non-technical stakeholders
• Quantify business impact (margin improvement, energy reduction, reliability gains)