Sales and Trading Data Jobs

1 job found

1mo ago

Sr. AI & Data Engineer–Trading Analytics

Shell Houston, TX

Houston, TX

1mo ago Closed

$149k - $223k/yr Full time Senior Ai Ml Analytics

This position is no longer accepting applications (closed Jul 11, 2026).

Job Description

Shell offers a competitive compensation package for a Senior AI and Data Engineer in Trading Analytics, based in Houston with a hybrid work arrangement. The role carries a salary range of USD 149,000 to 223,000 per year. You will collaborate with traders and analysts to design AI driven front office analytics and GenAI/agentic solutions for trading, backed by a comprehensive benefits program designed to support your wellbeing and growth.

Benefits

Medical coverage
Dental coverage
Vision coverage
Life Insurance
Business Travel Accident Insurance
Occupational Accidental Death Benefit
Company pension plan
401(k) plan
Paid vacation time (up to 6 weeks)
Paid holidays (up to 11)
Parental leave (16 weeks birthing; 8 weeks non-birthing)
Short-term disability leave (up to 26 weeks at 100% or 50%)
Long-Term Disability insurance
Financial reimbursement for adoption, wellness, education, and personal learning expenses
Discretionary long-term incentives

Responsibilities

Design and deliver AI driven analytics for traders and analysts, including seasonality analysis, correlations, regression models, forecasting, and scenario modelling over market pricing and fundamentals data
Collaborate with traders and analysts to translate ambiguous business questions into clear analytical problems and practical solutions
Communicate analytical outputs and AI generated insights to commercial stakeholders in a concise, actionable manner
Build and maintain scalable data pipelines on Databricks using PySpark/Spark, SQL, Delta Lake, and Unity Catalog
Support ingestion, modelling, and transformation of large scale time series pricing and fundamentals datasets
Optimize pipelines for performance, reliability, and cost efficiency, following platform and data governance standards
Build and enhance GenAI and agent based solutions to support trading analytics, including Retrieval Augmented Generation, prompt engineering, agent orchestration using LangGraph, tool calling and guardrails
Integrate LLM based workflows with structured trading and market data to augment analysis, insight generation, and decision support
Prototype solutions quickly, gather user feedback, and help harden selected use cases for production deployment
Contribute to production ready analytics and AI solutions with testing, documentation, versioning, and basic observability
Follow established CI/CD and DevOps practices, including use of Git based workflows and automated testing
Support governance requirements such as PII handling, data lineage, and auditability in line with Trading & Supply standards

Requirements

Must be legally authorized to work in the United States on a full-time basis for any employer (no Shell sponsorship required)
Bachelor’s degree or equivalent relevant years of experience
At least 10 years of relevant experience
Hands on experience with Databricks and/or Spark (PySpark, SQL, Delta Lake; Unity Catalog desirable)
Proven data engineering skills, including pipeline development, data modelling, and performance optimization
Solid foundation in statistics, econometrics, or data science, with applying these techniques to time-series or market style datasets
Practical experience building or contributing to LLM based solutions, including prompt engineering and retrieval based approaches
Familiarity with GenAI frameworks and tooling (e.g., LangGraph or similar orchestration patterns)
Experience working in collaborative engineering teams using Git and CI/CD pipelines
Strong communication skills and the ability to work directly with analysts, traders, and other business stakeholders

Technologies

Databricks
PySpark
Spark
SQL
Delta Lake
Unity Catalog
LangGraph

Additional preferred qualifications

Exposure to commodity or financial trading environments
Understanding of market fundamentals, supply demand dynamics, or risk concepts
Experience with MLflow, feature stores, or vector databases
Familiarity with regulated or risk sensitive environments

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