# Prediction Markets Quantitative Engineer

**Company:** [G-20 Group](http://jobs.workable.com/companies/g59Yg28hBL2qUYagL2ghyB.md)
**Location:** London, United Kingdom
**Workplace:** on site
**Employment type:** Full-time

[Apply for this job](http://jobs.workable.com/view/9165d2ba-9351-4a0a-97b6-822f47c8be3e)

## Description

**About G20 Group** 

G-20 Group is a leading cross-asset trading firm active in delta-one and derivatives markets. Established in 2010, G-20 offers liquidity solutions, treasury management, and institutional advisory services. We are supported by an outstanding team of professionals, with a robust global presence in EMEA, Americas, and APAC.

**Role Overview** 

We are hiring a **Prediction Markets Quant Engineer** to build research and trading infrastructure for operating in prediction markets (event contracts) across multiple venues. You will design models that estimate event probabilities, detect mispricing, size positions, and manage risk – then translate them into reliable systems that run end-to-end (data → forecasting → execution → monitoring). 

This role sits at the intersection of quant research, engineering, and market microstructure, and is ideal for someone who enjoys shipping robust systems as much as developing models. 

**Responsibilities**

_**Modeling & Research**_ 

-   Develop probabilistic models to forecast outcomes of real-world events (e.g., elections, macro releases, sports, policy decisions, industry milestones). 

-   Combine heterogeneous signals (time series, text/news, market data, polling/alternative data, fundamentals, expert priors) into calibrated probability estimates. 

-   Build pricing and edge frameworks: fair value, uncertainty bands, expected value, and model drift/regime diagnostics. 

-   Design evaluation methods (proper scoring rules like log loss/Brier score, calibration curves, back-tests with realistic costs and constraints). 

_**Trading & Market Design (Applied)**_ 

-   Identify and exploit mis-pricings across contracts/venues; design cross-market arbitrage and relative-value strategies where feasible. 

-   Build position sizing and risk frameworks (Kelly variants, drawdown/risk budgets, scenario stress tests, liquidity/impact-aware sizing). 

-   For multi-outcome markets: enforce probability coherence (no-arb constraints, normalization) and portfolio optimization across correlated contracts. 

_**Engineering & Production**_ 

-   Build data pipelines and real-time services for ingesting, cleaning, and versioning market + external data. 

-   Implement execution tooling: order management, smart routing (where applicable), monitoring, and automated safeguards. 

-   Create dashboards/alerts for performance, exposure, model health (calibration, drift), and operational integrity. 

-   Ensure reproducibility: experiment tracking, model registry, CI/CD, and robust testing. 

_**Collaboration & Governance**_ 

-   Work closely with trading/risk/compliance stakeholders to translate research into controlled deployment. 

-   Document models, assumptions, failure modes, and operating procedures; participate in incident reviews and continuous improvement.

## Requirements

-   Degree in Quantitative Finance, Mathematics, Computer Science, Statistics, or a related quantitative field. 
-   Strong engineering skills with Python (required); experience with production systems and data engineering. 
-   Solid foundation in statistics, probability, and machine learning (calibration, uncertainty, causal pitfalls, time-series). 
-   Experience building backtests and evaluating predictive models with appropriate metrics (e.g., log loss/Brier, calibration). 
-   Familiarity with trading concepts: expected value, position sizing, risk budgeting, correlation, liquidity constraints. 
-   Ability to communicate clearly about model assumptions, limitations, and risk. 
-   Some schedule flexibility may be required around major event windows 
-   Self-motivated, detail-oriented, and comfortable working in a dynamic, startup-like environment. 

**Preferred / Desirable Experience** 

-   Prior work in forecasting, sports analytics, political modeling, event-driven trading, or market-making/liquidity modeling. 
-   Experience with NLP for news/social/media signals; knowledge graphs or information retrieval for event resolution. 
-   Knowledge of prediction market mechanics (order books vs AMMs, fee structures, market manipulation/anti-manipulation signals). 
-   Proficiency with SQL; experience with streaming systems (Kafka), workflow orchestration (Airflow), and cloud (AWS/GCP/Azure). 
-   Experience with Bayesian methods, probabilistic programming (Stan/PyMC), or ensemble methods. 
-   Familiarity with rigorous experimentation: online/offline evaluation, data leakage prevention, and model governance. 

**Tech Stack** 

-   Python, SQL, pandas/numpy/scipy, PyTorch/sklearn 

-   Airflow/dbt, Kafka (or equivalents), Postgres/BigQuery 

-   Docker, Kubernetes (optional), CI/CD (GitHub Actions) 

-   Observability: Prometheus/Grafana, OpenTelemetry (or equivalents) 

**Locations and Right to work**: This role can be based out of our Zurich, London, New York or Hong Kong office. Only candidates who possess the pre-existing right to work in one of the locations above without company sponsorship need apply.  

Join G-20 and be a part of a team that is at the forefront of financial markets, driving innovation and excellence in the sector.