Lead Data Scientist(Oil and Gas Industry)
Not Disclosed•Full-TimeOn-site
location_on1769, San Jacinto Street, Downtown, Houston, Harris County, Texas, 77002, United States
About Tiger Analytics
Tiger Analytics is a fast-growing advanced analytics consulting firm trusted by multiple Fortune 500 companies to generate business value from data. Our consultants bring deep expertise in Data Science, Machine Learning, and AI, a reputation recognized by market research leaders like Forrester and Gartner. We are building the best global analytics consulting team in the world and are seeking top-notch talent to join our mission.
About the Role
We are seeking a Data Scientist with strong downstream refining experience to drive data-driven insights across refinery operations, economics, and reliability. This role exists to partner closely with process engineers, operations, planning, maintenance, and commercial teams to optimize refinery performance using advanced analytics, machine learning, and domain-informed modeling.
In this position, you will tackle high-impact problems such as yield optimization, energy efficiency, unit reliability, predictive maintenance, and margin improvement. Your work will involve turning complex refinery data into actionable intelligence that directly influences business outcomes.
Career Opportunity
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.
Equal Opportunity
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.
Work location
Work model: On-site
1769, San Jacinto Street, Downtown, Houston, Harris County, Texas, 77002, United States
Houston, Texas
Key Responsibilities
- check_circleDevelop, validate, and deploy statistical, ML, and optimization models for refining operations
- check_circleApply time-series analysis to high-frequency plant data from DCS and historians
- check_circlePartner with process engineers and operations teams to translate refinery problems into analytical solutions
- check_circleInterpret model results in the context of refinery economics, safety, and operability
- check_circleCommunicate insights clearly to technical and non-technical stakeholders
- check_circleQuantify business impact including margin improvement, energy reduction, and reliability gains
- check_circleBuild models for unit performance, energy efficiency, yield optimization, and predictive maintenance
- check_circleIncorporate first-principles knowledge into data models to ensure alignment with process limits
Requirements
- verifiedBachelor's or Master's degree in Data Science, Chemical Engineering, Applied Mathematics, Statistics, or related field
- verified3–8+ years of experience applying data science in downstream refining or closely related process industries
- verifiedStrong proficiency in Python or R
- verifiedExperience with time-series data and industrial process data
- verifiedSolid understanding of refining processes and unit operations
- verifiedExperience working with historians (PI), SQL databases, and unstructured data
Nice to Have
Advanced degree (MS or PhD). Familiarity with optimization techniques (LP/NLP), digital twin or hybrid physics + ML models, and cloud platforms (AWS, Azure, GCP).
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Skills, education and keywords
Skills: Data Science, Machine Learning, Ai, Statistical Models, ML Models, Optimization Models, Time-Series Analysis, Python, R, SQL.
Education: Bachelor's degree in Data Science, Chemical Engineering, Applied Mathematics, Statistics, or related field required; Master's degree in Data Science, Chemical Engineering, Applied Mathematics, Statistics, or related field required; PhD preferred.
Frequently asked questions about Lead Data Scientist(Oil and Gas Industry) at Tiger Analytics
What does a Lead Data Scientist(Oil and Gas Industry) at Tiger Analytics do?expand_more
In this Lead Data Scientist(Oil and Gas Industry) at Tiger Analytics role, you will develop, validate, and deploy statistical, ml, and optimization models for refining operations; apply time-series analysis to high-frequency plant data from dcs and historians; partner with process engineers and operations teams to translate refinery problems into analytical solutions; and interpret model results in the context of refinery economics, safety, and operability.
What are the requirements for this Lead Data Scientist(Oil and Gas Industry) role?expand_more
Tiger Analytics is looking for candidates who meet the following 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; Experience with time-series data and industrial process data; Solid understanding of refining processes and unit operations; and Experience working with historians (PI), SQL databases, and unstructured data.
Where is the Lead Data Scientist(Oil and Gas Industry) role at Tiger Analytics located?expand_more
Lead Data Scientist(Oil and Gas Industry) at Tiger Analytics is based in 1769, San Jacinto Street, Downtown, Houston, Harris County, Texas, 77002, United States. This is a on-site role.
Is this Lead Data Scientist(Oil and Gas Industry) job remote, hybrid, or on-site?expand_more
Tiger Analytics has listed this Lead Data Scientist(Oil and Gas Industry) role as on-site.
How much experience is required for this Lead Data Scientist(Oil and Gas Industry) role?expand_more
Lead Data Scientist(Oil and Gas Industry) at Tiger Analytics typically requires 3–8 years of relevant experience at the senior level.
What skills do you need for the Lead Data Scientist(Oil and Gas Industry) role at Tiger Analytics?expand_more
Key skills for Lead Data Scientist(Oil and Gas Industry) at Tiger Analytics include Data Science; Machine Learning; Ai; Statistical Models; ML Models; Optimization Models; Time-Series Analysis; and Python.
What education is required for Lead Data Scientist(Oil and Gas Industry) at Tiger Analytics?expand_more
Educational requirements for this role: Bachelor's degree in Data Science, Chemical Engineering, Applied Mathematics, Statistics, or related field required; Master's degree in Data Science, Chemical Engineering, Applied Mathematics, Statistics, or related field required; and PhD preferred.
What category does the Lead Data Scientist(Oil and Gas Industry) role belong to?expand_more
Lead Data Scientist(Oil and Gas Industry) at Tiger Analytics is part of the energy, oil & gas job category on Recrutus.