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Quant Developer - Systematic Commodities Hedge Fund

Moreton Capital Partners Open · verified Oct 24, 2026
On-site Mexico City Full-time Mid-level Data Science

At a glance

Mid-level Data Science role at Moreton Capital Partners. Mexico City · full-time.

Pay not stated Data Science salary

RoleData Science
SeniorityMid-level
LocationMexico City
WorkplaceOn-site
EmploymentFull-time
PostedAug 29, 2026 · 8w ago
Role brief

dataroles summary, based on the employer's posting.

What you'll do

  • Build pipelines that bring futures prices and alternative, including unstructured, data into research workflows
  • Develop LLM workflows for research, data handling and internal tools, then improve how they run
  • Strengthen backtests with event-driven simulations, realistic trading costs, walk-forward checks and portfolio analysis
  • Move research signals into live trading with CI/CD, monitoring and version control; track data quality, latency and model drift
  • Work with researchers to turn hypotheses into testable experiments and improve their computational processes

What you bring

  • You write clean, testable Python and SQL code
  • You have data engineering experience with Airflow, Snowflake, pandas, polars or similar workflows
  • You have worked with systematic trading, backtesting or market-data pipelines
  • You hold a bachelor’s degree in computer science, computer engineering or another computationally intensive subject

Who this fits

This mid-level role suits a hands-on quant developer who can work independently across research infrastructure and production systems. The job is based on-site in Mexico City.

From the employer

Quant Developer – Systematic Commodities Hedge Fund

Moreton Capital Partners is seeking a talented Quant Developer to join our team. We are live trading across global commodity futures, supported by an investment process rooted in machine learning.

This is a unique opportunity to work directly with the global team, owning infrastructure that takes research ideas to production in a fast-moving, real capital environment.

Key Responsibilities

  • Build and maintain data pipelines ingesting futures and alternative datasets (from price data from vendor feeds to unstructured data).
  • Build and improve LLM-based workflows that support research, data processing, and internal tooling.
  • Improve backtesting framework (event-driven simulations, realistic slippage/costs, walk-forward validation, portfolio performance analysis).
  • Support research tooling: feature libraries, experiment tracking, artefact storage.
  • Machine learning cloud and local execution and optimization setup.
  • Productionize signals into the live trading stack with CI/CD, monitoring, and version control.
  • Develop dashboards and alerting for data quality, latency, and model drift.
  • Collaborate with researchers to translate hypotheses into robust, testable experiments, as well as enhance proposed process computationally.

Requirements

  • Fluency in Python and SQL; clean, testable code is a must.
  • Experience with data engineering (Airflow, Snowflake, pandas, polars workflows).
  • Prior exposure to systematic trading, backtesting, or market data pipelines.
  • Familiarity with cloud environments (AWS), containers (Docker), and CI/CD.
  • Self-starter with the ability to work autonomously in a lean, high-ownership environment.
  • Bachelors in CS/Comp-Eng or computationally heavy subject matter, and ideally, a minor in Finance.

Bonus points for:

  • Commodities or macro markets exposure.
  • Systematic medium term investment exposure.
  • Experience with ML Ops tools (MLflow, Weights & Biases), feature stores, or model monitoring.
  • Front-end skills (TypeScript/React) to help build researcher dashboards.

Benefits

  • Impact from day one: You’ll be building mission-critical infrastructure for a fund that is already live trading for large institutional investors.
  • Direct exposure: Work alongside the CIO and senior researchers, with a direct line to decision-making.
  • Learning curve: Deep exposure to commodity markets, ML research workflows, and institutional-grade trading systems.
  • Growth trajectory: Clear path to increased scope and compensation as the fund scales with institutional AUM.
  • Attractive compensation: Highly competitive base salary and annual bonus that scales as the business grows.
  • Positive, inclusive and encouraging work environment.
Opportunity

Data Science work from LinkedIn, X, Reddit and Upwork, in one list.

Each morning you get everything that fits, strongest first.

Data Science

Find me work

By the team behind dataroles.

Find me work
Opportunity

Data Science work from LinkedIn, X, Reddit and Upwork, in one list.

Each morning you get everything that fits, strongest first.

Find me work

By the team behind dataroles.