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Statistics Internship Jobs in Texas (NOW HIRING)

Our internship is a key pipeline into full-time roles in the AEDP - our primary pathway for ... statistics, finance, economics, or data analytics. * Minimum 3.2 GPA. * Uses data to solve ...

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Statistics Internship information

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How much do statistics internship jobs pay per hour?

As of Sep 4, 2026, the average hourly pay for statistics internship in Texas is $16.12, according to ZipRecruiter salary data. Most workers in this role earn between $13.41 and $17.93 per hour, depending on experience, location, and employer.

What is a statistics internship?

A statistics internship is a temporary position that allows students or recent graduates to gain practical experience by applying statistical methods and data analysis techniques in a real-world setting. Interns typically work under the supervision of experienced statisticians or data analysts, assisting with data collection, cleaning, analysis, and interpretation. The internship helps individuals develop technical and professional skills relevant to fields such as data science, research, business analytics, healthcare, and more. It also offers valuable opportunities for networking and can often lead to full-time employment in the field.

What are some typical projects or tasks that a statistics intern works on during their internship?

Statistics Interns often assist with data collection, cleaning, and analysis for ongoing research or business projects. They may use statistical software to run analyses, create visualizations, and interpret results under the guidance of senior analysts or statisticians. Interns frequently collaborate with cross-functional teams, such as data science, engineering, or marketing, to provide insights that inform decision-making. This hands-on experience helps interns develop practical skills and gain exposure to real-world applications of statistical methodologies.

What are the key skills and qualifications needed to thrive as a statistics intern, and why are they important?

To thrive as a Statistics Intern, you need a solid understanding of statistical methods, data analysis, and foundational coursework in mathematics or statistics. Familiarity with statistical software such as R, Python, SAS, or SPSS, along with proficiency in Excel, is typically expected. Attention to detail, problem-solving ability, and clear communication skills help interns interpret data accurately and share findings effectively. These competencies are essential for supporting data-driven decision-making and contributing valuable insights to projects.

What is the difference between Statistics Internship vs Data Analyst?

AspectStatistics InternshipData Analyst
Required CredentialsTypically pursuing or recent graduate in statistics, mathematics, or related fieldBachelor's or master's in statistics, data science, or related field
Work EnvironmentInternship setting, often in research or academic environmentsFull-time role in corporate, tech, or finance industries
Employer & Industry UsageInternship programs in universities, research institutions, or companiesBusinesses, government agencies, and consulting firms
Common Search & ComparisonYesYes

The main difference between a Statistics Internship and a Data Analyst role lies in experience level and employment status. Internships are typically temporary positions for students or recent graduates gaining practical experience, while Data Analysts are full-time professionals responsible for analyzing data to inform business decisions. Both roles require strong statistical skills, but internships focus on learning and development, whereas Data Analysts perform ongoing data analysis tasks.

What are the most commonly searched types of Statistics jobs in Texas?

The most popular types of Statistics jobs in Texas are:

What cities in Texas are hiring for Statistics Internship jobs?

Cities in Texas with the most Statistics Internship job openings:

Infographic showing various Statistics Internship job openings in Texas as of August 2026, with employment types broken down into 8% Internship, 63% Full Time, 27% Part Time, 1% Temporary, and 1% Contract. Highlights an 77% Physical, 2% Hybrid, and 21% Remote job distribution, with an average salary of $33,535 per year, or $16.1 per hour.

Commodity Trading Analytics Internship Program

Engelhart

Houston, TX

$68K - $100K/yr

Full-time, Internship

Posted 7 days ago


Job description

Commodity Trading Analytics Internship Program

Six-Month Internship: June 15 - December 16, 2027
Houston and NYC (full-time, on site)
Closing Date: September 13, 2026

Salary Range: $68,000 - $100,000 annualised

Engelhart's Internship Program offers a unique opportunity to gain hands-on experience with North American Front Office teams working at the intersection of commodities markets, data, analytics, modelling and trading. Interns will contribute to practical, data-led projects that support commercial decision-making, forecasting, risk analysis and market insight across power, gas, and broader commodity markets. To be eligible for consideration, applicants must have the right to work in the United States for the full duration of the internship and be available to commence their six-month internship on site from June 15, 2027.

The internship opportunities covered by this advert are as follows:

  • Quantitative Development Intern, Houston
  • Quantitative Research Intern, Houston
  • NA Power & Gas Analytics Intern, Houston
  • FTR Analytics Intern, Houston
  • NA Structured Products & Origination Analytics Intern, Houston
  • West Power Analytics Intern, New York

Applicants may indicate areas of particular interest during the process. All roles require strong analytical and technical skills, with opportunities spanning data analysis and visualisation, machine learning, forecasting, financial modelling, statistics/econometrics and quantitative modelling. Final allocation will take into account business need, candidate profile and overall fit.

Please note: annualized salary will depend on location, role requirements and the candidate's overall profile, including educational background, relevant skills and experience. Final compensation will be determined as part of the offer process, and the upper end of the advertised range is expected to apply only in limited cases.

What You Can Expect

  • Hands-on experience on a commodities trading desk, working on analytical projects with real commercial relevance.
  • Exposure to how data, analytics, visualisation, forecasting, machine learning and modelling support trading and commercial decisions across North American power, gas and broader commodity markets.
  • Close learning and mentorship from experienced front office professionals, including direct exposure to desk leadership.
  • A structured six-month internship within a global commodities business, combining cohort-based learning with deep desk integration.

Example Project Areas

Depending on the internship opportunity, projects may include data analysis and visualisation, building models and analytics, improving machine learning models for power prices, gas demand or renewables, working on Monte Carlo simulations and time-series analysis, supporting pricing, valuation, optimisation or portfolio construction problems, developing AI-enabled tools, improving pre-trade pricing or portfolio management of structured origination positions, and producing insights that help inform front office trading and commercial decisions.

Key Responsibilities

  • Work closely with a Houston or New York front office team on data-led projects within North American power and gas, FTR, structured products and origination, or West Power.
  • Analyse large market, trading and fundamental datasets to identify insights that support forecasting, valuation, risk analysis and commercial decision-making.
  • Develop, test or improve analytical tools, models, dashboards or workflows using Python and other relevant tools.
  • Support forecasting, financial modelling, machine learning, statistical/econometric analysis, Monte Carlo simulation or quantitative optimisation activities, depending on desk requirements.
  • Conduct market research and present findings clearly to technical and commercial stakeholders.
  • Contribute to day-to-day desk activity, building practical understanding of how analytics supports trading and portfolio decisions.

About You

Across our 2027 internship program, we are looking for motivated, intellectually curious individuals who are keen to apply strong analytical, quantitative and technical skills to real business challenges in power, gas, renewables and broader commodity markets. You should be comfortable working with data, learning quickly, and collaborating closely with front office teams in a fast-paced trading environment.

The following experiences and skills are essential for application:

  • Strong academic background in a quantitative discipline such as Computer Science, Data Science, Engineering, Physics, Mathematics, Statistics, Economics, Finance or a related STEM field.
  • Strong Python skills, with the ability to analyze, manipulate and interpret complex data sets.
  • Excellent analytical and problem-solving skills, with a structured and detail-oriented approach.
  • Evidence of applying data analysis, coding or quantitative methods to a practical problem, gained through academic projects, internships, research, personal projects or other relevant experience.
  • Demonstrable interest in commodities, power or financial markets, including an ability to discuss relevant market developments, gained through academic study, projects, internships, research or personal interest.
  • Business-level English proficiency.
  • Right to work in the United States for the full duration of the internship.
  • Effective communication skills and a collaborative mindset, with the confidence to work closely with commercial and technical stakeholders.
  • Intellectual curiosity, with the motivation to understand how market fundamentals, data and commercial decisions interact.

The following would be advantageous:

  • Experience with SQL, relational databases, data visualisation, machine learning, AI/LLM tools, Git, financial modelling, advanced Excel/VBA, or working with large market
  • Exposure to forecasting models, valuation, statistics/econometrics, time-series analysis, stochastic modelling, Monte Carlo simulation, optimisation, MILPs or portfolio construction.