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Adolfo Villalobos

Software Engineer | Quant & Data Engineering
Melbourne, Australia
linkedin.com/in/adolfovillalobos
github.com/AdolfoVillalobos
adolfo.villalobos.vega@gmail.com
+61 451 165 153
Innovative and results-driven Software Engineer with a strong foundation in quantitative trading, machine learning, and high-frequency trading systems. Proven ability to develop low-latency trading algorithms, optimize real-time data pipelines, and apply advanced mathematical models to financial markets. Experienced in Python, Rust and Go, with hands-on expertise in algorithmic trading, convex optimization, and distributed systems. Adept at building scalable solutions for competitive financial markets and collaborating with cross-functional teams to drive the evolution of proprietary trading strategies.

Work Experience

Quant (Software Engineer)

Orionx.com

Jul 2021 - Current
  • Developed a low-latency automated trading system in Python and Kubernetes using an event-driven architecture to execute market-making, liquidity mirroring, and other strategies. This resulted in a 2X increase in administered capital on a 3-year horizon.
  • Trained and deployed automated clustering algorithms to identify under-arbitraged markets, resulting in a 500% increase in trading volume through the expansion of active pairs.
  • Designed a triangular arbitrage algorithm using the Bellman-Ford method, resulting in a $100k monthly increase in trading volume with a 67% win rate due to improved route efficiency and reduced latency.
  • Developed an online learning algorithm to optimize bid/ask price estimates, reducing slippage risk by 50% during volatile market conditions.
  • Trained and deployed machine learning algorithms for anomaly detection in online prices, resulting in a 0% reduction in data errors.

Data Engineer (Software Engineer)

Orionx.com

Mar 2021 - Current
  • Engineered and maintained over 100 ETL jobs to support algorithmic trading strategies and automated reporting in Airflow, streamlining data processing for optimal decision-making.
  • Trained and served machine learning models using MLflow instance, supporting five production and research use cases.
  • Implemented mission-critical APIs using FastAPI, Pydantic, SQLModel, and Alembic, improving trade volume by 300% and enhancing data integrity across trading platforms.
  • Integrated MyPy, Pandas, Pydantic, and Pytest into CI/CD pipelines and live production environments, resulting in decreased deployment errors and smooth operation of trading applications.

Lead Data Scientist

Agrosuper

Mar 2019 - Jun 2020
  • Built a random forest ensemble model to predict risk factors in slaughterhouse workers, resulting in a classifier with 98% out-of-sample accuracy and a 30% reduction in risk exposure
  • Designed and led A/B testing experiments, contributing to an 8% reduction in operational time and improved process efficiency.

Data Scientist

IMC UC

Mar 2018 - Jul 2019
  • Developed regression models to forecast agricultural yields, reducing prediction error from 20% to 2%, allowing for more accurate financial planning and resource allocation.

Data Analyst Intern

Chilean Central Bank

Dec 2017 - Mar 2018
  • Conducted in-depth analysis of household debt data, identifying the top three factors influencing debt underreporting, which led to policy refinements.

Education

M. Sc. in Engineering (Data Science)

Pontificia Universidad Católica (Santiago, Chile)

Relevant Coursework: Supervised and Unsupervised Learning, Convex Optimization, Deep Learning, Foundations of Reinforcement Learning, Transformers Architecture

2019 - 2021

B.Sc. in Engineering (Applied Mathematics)

Pontificia Universidad Católica (Santiago, Chile)

2013 - 2018

Technical Skills

Programming Languages
Python (expert), Go, Rust, SQL.
Machine Learning & Optimization
Convex Optimization, Reinforcement Learning, Bayesian Inference, Machine Learning
Algorithms & Data Structures
Graph Algorithms, Linear Programming, Supervised & Unsupervised Learning
Distributed Systems & Cloud
AWS (ECS, EC2, EKS, Lambda, S3, VPC, ECR), Docker, Kubernetes, Linux, Airflow, Spark, PostgreSQL, Redis, MongoDB, ETL Pipelines
Development Tools
FastAPI, Pydantic, SQLModel, Alembic, CI/CD, Pytest