Lead Data Scientist(Oil and Gas Industry) job opportunity at Tiger Analytics Inc..

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Date2026-03-03T06:19:16.795Z bot
Tiger Analytics Inc. Lead Data Scientist(Oil and Gas Industry)
Experience: 8-years
Pattern: Full-time
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degreePhD
loacation Houston, United States Of America
loacation Houston....United States Of America

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. 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) 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 Preferred Qualifications 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.

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