Financial Mathematics Expert (Python, Quant Finance)

About OpenTrain

OpenTrain is a central job board for AI-training and data-labeling work. We aggregate roles from many AI companies and labeling platforms so contributors can find remote, flexible projects in one place instead of searching dozens of sites.

Creating an OpenTrain account is free and applying takes only a few minutes.

About AI Training and This Role

AI training (also called data labeling, annotation, or human feedback work) is the human-powered process that helps machine learning models learn and behave correctly. Projects range from writing and rating model outputs to building datasets and validating computations.

This assignment sits at the intersection of quantitative finance and AI training: you will author original, research-style computational problems and validate their solutions in Python so the material can be used for model fine-tuning, generation tasks, and evaluation.

The Role

We are seeking a Financial Mathematics Expert to design and verify complex, computationally intensive problem sets relevant to quantitative finance and financial modeling.

Work is remote and offered on a part-time, contractor basis. Expect to contribute 20+ hours per week and to produce problems whose solutions require advanced numerical methods and cannot be solved by hand.

• Employment type: Contractor, Part-time
• Time requirement: 20+ hours per week
• Data type: Text (problem statements, solution notebooks, reproducible docs)
• Labeling tasks include: TEXT_GENERATION, EVALUATION_RATING, and FINE_TUNING use cases
• Labeling software: OTHER (you will deliver Python-validated artifacts and documentation)

What You'll Do

Author original problem sets in topics such as stochastic processes, optimization, Monte Carlo simulation, time series, and risk modeling. Problems must be research-style and computationally intensive.

Implement and validate every solution in Python with clear, reproducible code and documentation so results can be rerun and reviewed.

• Design problems that require numerical methods, simulation, or optimization and cannot be solved analytically by hand
• Provide Python notebooks or scripts that reproduce results and include explanatory comments and expected outputs
• Create evaluation criteria and model-facing prompts or references for potential fine-tuning or generation tasks
• Prepare concise write-ups and sample solutions suitable for use in evaluation and model training pipelines

Requirements

You must meet the following minimum qualifications and provide the requested documentation.

CV must be in English and include an email address, phone number, and a declaration of your English proficiency level.

• Education: Bachelor’s degree or higher in finance, mathematics, statistics, or a related field
• Experience: At least 2 years of quantitative finance or modeling experience
• Technical skills: Strong Python skills and familiarity with NumPy, SciPy, Pandas, SymPy, and statistics/ML libraries
• Numerical methods: Experience with numerical methods and simulation (Monte Carlo, discretization, optimization, etc.)
• Annotation experience: Hands-on text annotation or review experience is required
• Additional: International or applied project experience is a plus

Compensation, Eligibility, and Restrictions

Pay is hourly in USD. The published range is $15–$60 per hour, with the listing rate up to $60/hr. You will be engaged as a contractor and paid per hour.

The role is open worldwide except for specific restricted locations listed below. Applicants located in restricted jurisdictions cannot be considered.

• Pay type: PAY_PER_HOUR, currency: USD, range: $15–$60/hr, listed rate: $60/hr
• Experience level: Intermediate
• Work model: Remote

Location Restrictions

Restricted locations for acquisition (applicants in these places cannot be considered): Iran, Cuba, North Korea, Syria, Sudan, Venezuela, Myanmar, Russia, Belarus, Palestine, Switzerland, China, Taiwan, Kenya, the following U.S. states: Alaska, Arkansas, California, Connecticut, Delaware, Georgia, Hawaii, Illinois, Indiana, Kansas, Louisiana, Maine, Maryland, Massachusetts, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, Ohio, Oregon, Tennessee, Utah, Vermont, Washington, West Virginia, and the following territories and jurisdictions: Antarctica, Aruba, Åland Islands, Saint Barthélemy, Bonaire, Sint Eustatius and Saba, Bouvet Island, Cocos (Keeling) Islands, Democratic Republic of the Congo, Cook Islands, Christmas Island, Western Sahara, Falkland Islands (Malvinas), French Guiana, Guadeloupe, South Georgia and the South Sandwich Islands, Heard Island and McDonald Islands, British Indian Ocean Territory, Northern Mariana Islands, Martinique, New Caledonia, Norfolk Island, Niue, French Polynesia, Saint Pierre and Miquelon, Pitcairn, Réunion, Saint Helena, Ascension and Tristan da Cunha, Svalbard and Jan Mayen, Sint Maarten (Dutch part), French Southern Territories, Tokelau, United States Minor Outlying Islands, Holy See, Virgin Islands (British), Wallis and Futuna, Mayotte.

How to Apply

To apply, create or sign in to your OpenTrain account and submit an English CV that states your level of English proficiency and includes your email address and phone number. Provide examples or summaries of relevant quantitative work (papers, notebooks, or sample problems) if available.

Applications should explicitly state your availability to work 20+ hours per week and confirm you can deliver Python-validated solutions with reproducible documentation.

• Application materials required: English CV (with email and phone), short statement of relevant experience, example notebooks or problem descriptions if available
• Selection process: applicants will be screened for qualifications and sample work; selected candidates may be asked to complete a short paid exercise

Who Should Apply

Apply if you are an intermediate-level quant with hands-on Python experience, comfortable implementing numerical methods and simulations, and eager to author rigorous, reproducible problem sets for AI training and evaluation.

This role suits practitioners who enjoy clear documentation, reproducible computation, and translating advanced mathematical ideas into well-specified, machine-usable tasks.