Lead Data Scientist(Oil and Gas Industry)

Tiger Analytics Inc. • Houston, TX

2d

About The Position

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.
This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.
Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.

Requirements

• Bachelor’s or Master’s degree in Data Science, Chemical Engineering, Applied Mathematics, Statistics, or related field
• 3–8+ years of experience applying data science in downstream refining or closely related process industries
• Strong proficiency in Python or R for data analysis and modeling
• Experience with time-series data and industrial process data
• Solid understanding of refining processes and unit operations
• Experience working with historians (PI), SQL databases, and unstructured data

Nice To Haves

• Advanced degree (MS or PhD)
• Familiarity with: Optimization techniques (LP/NLP) Digital twin or hybrid physics + ML models Cloud platforms (AWS, Azure, GCP)nced Data Scientists.

Responsibilities

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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level