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Internship Machine Learning Quant Jobs in Texas (NOW HIRING)

Vitol

Senior Machine Learning Engineer

Houston, TX · On-site

$117K - $154K/yr

The Machine Learning Engineer at Vitol has visibility and impact across the full project workflow ... quantitative field * Fluency in Python for both data science and engineering purposes: clean ...

Anson McCade

Quant Research Analyst

Austin, TX · On-site

$120K - $150K/yr

Permanent Quant Research Analyst (Houston) Anson McCade are working with a Portfolio Manager based ... Machine Learning experience is preferred * An advanced degree, preferably a Master's degree or ...

Capital One Group

Lead Machine Learning Engineer

Plano, TX · On-site

$179 - $205/hr

## Lead Machine Learning EngineerApplylocations: Plano, TX: McLean, VAtime type: Full timeposted on ... Internship experience does not apply)*** **At least 4 years of experience programming with Python ...

New

Capital One

Lead Machine Learning Engineer

Plano, TX · On-site

$98K - $129K/yr

Lead Machine Learning Engineer Join the Dealer Tech division within Capital One's Financial ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

Socket.dev

Lead Machine Learning Engineer

Plano, TX · On-site

$179 - $205/hr

Lead Machine Learning Engineer Join the Dealer Tech division within Capital One's Financial ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

New

Capital One

Lead Machine Learning Engineer

Plano, TX

$98K - $129K/yr

Lead Machine Learning Engineer Join the Dealer Tech division within Capital One's Financial ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

Vitol

Senior Machine Learning Engineer

Houston, TX · On-site

$116K - $154K/yr

... quantitative field • Fluency in Python for both data science and engineering purposes: clean ... machine learning or statistical models, with a proven track record of delivering end-to-end ...

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All JobsMachine Learning Quant JobsInternship Machine Learning Quant JobsInternship Machine Learning Quant Jobs in Texas

Internship Machine Learning Quant information

What is the difference between Internship Machine Learning Quant vs Data Scientist Intern?

AspectInternship Machine Learning QuantData Scientist Intern
Required CredentialsStrong programming skills, basic finance knowledge, coursework in machine learningStatistics, programming, domain knowledge, coursework in data analysis
Work EnvironmentFinancial firms, hedge funds, quantitative trading teamsTech companies, startups, research labs
Industry UsageFinance, trading, quantitative researchTechnology, marketing, healthcare analytics
Common Search IntentInternship roles in finance with machine learning focusInternship roles in data science across industries

Internship Machine Learning Quant roles typically focus on applying machine learning techniques to financial data within trading and investment firms. Data Scientist Intern positions are broader, spanning various industries like tech and healthcare, emphasizing data analysis and modeling. While both require programming and analytical skills, the finance-specific knowledge is more critical for Machine Learning Quant internships.

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Senior Machine Learning Engineer

Vitol

Houston, TX • On-site

$117K - $154K/yr

Other

Re-posted 15 days ago

Job description

Company Description
Vitol is an energy and commodities company with revenues of $400 billion in 2023; its primary business is the trading and distribution of energy products globally - it trades over seven million barrels per day of crude oil and products and, at any time, has 250 ships transporting its cargoes.
Vitol's clients include national oil companies, multinationals, leading industrial companies and utilities. Founded in Rotterdam in 1966, today Vitol serves clients from some 40 offices worldwide and is invested in energy assets globally including 16mm3 of storage, 480kbpd of refining capacity, and 7,000 service stations. To date, we have committed over $2.5 billion of capital to renewable projects, and are identifying and developing low-carbon opportunities around the world. Learn more about us here.
This Role is located in Houston, TX - In office 5x a week
Job Description
As our portfolio of work continues to grow, we are looking for an experienced Machine Learning Engineer to join our data science and machine learning team. The individual will work closely with the data and machine learning specialists, software engineers and commercial teams to deliver machine learning models and applications. We work across the trading business, operations, and other support functions; so the individual will need to be comfortable working with a variety of stakeholders and technologies.
The Machine Learning Engineer at Vitol has visibility and impact across the full project workflow: from working with business stakeholders to help define the project, to data collation and processing, exploratory analysis, model selection and tuning, and implementation of production models.
The successful candidate will join a team of experienced, collaborative practitioners, who are (pragmatically) solving some of the most challenging and impactful problems the energy industry is facing; as well as pushing the boundaries around the 'art of the possible'.
Core Responsibilities include:
  • Design, develop, and deploy end-to-end machine learning and data science solutions across our wider business activities (including trading, operations, and support functions) - from raw data ingestion through to production-grade models and monitoring
  • Drive adoption and development of the firm's internal GenAI chat platform as one of the technical leads, extending its capabilities through new integrations, data connectors, and domain-specific prompt engineering; work closely with trading desks and operational teams to identify high-value use cases, embed the tool into day-to-day workflows, and ensure outputs are robust, and trusted by end users.
  • Apply a broad range of modelling techniques - including time-series forecasting, NLP, classification, and generative AI - to commodity pricing, supply/demand signals, trade flow analysis, and operational optimization problems
  • Own the full data science lifecycle on assigned projects: data sourcing and cleaning, exploratory analysis, feature engineering, model selection and validation, deployment, and ongoing performance monitoring
  • Build and maintain robust, well-tested, production-quality code; contribute to shared infrastructure including ML pipelines, data orchestration, and model serving layers
  • Integrate ML and GenAI outputs into existing trading systems, dashboards, and workflows; work with software engineers to ensure reliable, scalable adoption across the business
  • Communicate analytical findings and model outputs clearly to non-technical stakeholders; present results, assumptions, and limitations in a manner that supports confident commercial decision-making
  • Actively participate in code reviews, experiment design, and tooling decisions; mentor colleagues and help raise the overall standard of analytical and engineering practice across the team

Qualifications
  • Master's degree or equivalent in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field
  • Fluency in Python for both data science and engineering purposes: clean, modular, well-documented code, with strong understanding of software engineering best practices including version control, testing, and code review
  • 5+ years of industry experience developing and deploying machine learning or statistical models, with a proven track record of delivering end-to-end solutions in production environments
  • Demonstrable experience applying a broad range of ML methodologies (supervised and unsupervised learning, time-series modelling, NLP/LLMs, optimization) to real-world business problems
  • Strong proficiency with ML frameworks (e.g. PyTorch, scikit-learn, Transformers) and experience building or consuming LLM-based pipelines and GenAI applications
  • Experience with cloud platforms (AWS preferred) and modern MLOps practices: containerization (Docker/Kubernetes), CI/CD, data pipeline orchestration (e.g. Airflow, Dagster), and model serving
  • Strong analytical and problem-solving ability: capable of defining and scoping open-ended problems, proposing sound methodological approaches, and defending modelling choices with rigorous reasoning
  • Excellent written and verbal communication skills, with the confidence to present model outputs, caveats, and commercial implications clearly to non-technical audiences including traders and senior management
  • Genuine intellectual curiosity about commodities markets, global energy flows, and the commercial dynamics of trading; willingness to develop domain knowledge as part of the role

Desirable Experience
  • Experience in the energy or commodities trading industry, with knowledge of financial markets and trading concepts
  • Experience surfacing ML outputs through interactive tools (e.g. Dash, Streamlit, or similar) and presenting use cases to non-technical audiences, including traders and senior management
  • Time-series modelling in a trading or financial context, including both ML-based and econometric approaches (e.g. ARIMA, cointegration, regime-switching models)
  • Data orchestrators (Airflow, Dagster) and cloud-based ETL/ELT pipelines

Additional Information
Personal Characteristics
  • A self-motivated individual who thrives on seeing the results of their work make an impact in the business
  • Pragmatic and delivery-focused: comfortable navigating ambiguity, balancing rigor with speed, and making sound judgements under uncertainty
  • Methodical and detail-oriented: rigorous in experimental design, data validation, and code quality, with a disciplined approach to documenting assumptions and results
  • Resourceful, able to think creatively and adapt in a dynamic environment
  • Team player, with an open non-political style and a high level of integrity
  • Desire to be a thought-partner in a fast-growing team, and make an impact at a business that sits at the heart of the world's energy flows

Work Environment
  • This job operates in a professional office environment. Because of the collaborative, fast-paced, and high energy nature of our business, Vitol requires team members to work from our fully-equipped office.

What we offer
  • Competitive salary and benefits package
  • Large diversity of projects with real-world impacts on a truly global scale
  • Entrepreneurial environment within a flat hierarchy, where great ideas come to life quickly
  • Close collaboration with various business units across our key regions (eg. London, Singapore, Houston, Geneva)
  • A highly motivated DS and ML team comprised of experienced individuals with a supportive attitude and great team spirit
  • Being part of the energy transition through increased emphasis on renewable & alternative energy sources at a pivotal moment in the industry
  • Strong management commitment to incorporating machine learning into the future of Vitol's operations

All your information will be kept confidential according to EEO guidelines.

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