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For Companies At a Glance
- Tasks: Build cutting-edge models and systems for prediction markets across multiple venues.
- Company: Join G-20 Group, a pioneer in Quantitative Trading with a startup vibe.
- Benefits: Competitive salary, dynamic work environment, and opportunities for growth.
- Why this job: Make an impact in financial markets while working with innovative technologies.
- Qualifications: Degree in a quantitative field and strong Python engineering skills required.
- Other info: Flexible schedule and collaborative culture in a fast-paced setting.
The predicted salary is between 43200 - 72000 £ per year.
About G20 Group
The G-20 Group is a pioneer in Quantitative Trading systems in cross-asset markets. Headquartered in Switzerland, we operate at the intersection of Quantitative Research, Software Engineering and Trading. The team combines a startup mindset with extensive experience in proprietary Trading, Technology and Quantitative Finance.
Role Overview
We are hiring a Prediction Market 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).
- 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.
- 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.
- 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.
Qualifications
- 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)
Deadline for application: Jan 4, 2025
Locations and Right to work
This role will be based in 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.
Prediction Markets Quantitative Engineer (London) employer: G-20 Group
Contact Detail:
G-20 Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Prediction Markets Quantitative Engineer (London)
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to quantitative finance or predictive modelling. This gives potential employers a taste of what you can do beyond just a CV.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice coding challenges and be ready to discuss your thought process when tackling complex problems.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining our team at G20 Group.
We think you need these skills to ace Prediction Markets Quantitative Engineer (London)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Prediction Markets Quantitative Engineer role. Highlight your engineering skills, quantitative finance knowledge, and any relevant projects you've worked on.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about prediction markets and how your background makes you a great fit. Be specific about your experience with probabilistic models and trading concepts.
Showcase Your Technical Skills: Since this role requires strong engineering skills, don’t forget to mention your proficiency in Python and any experience with data pipelines or production systems. We want to see how you can contribute to our tech stack!
Apply Through Our Website: We encourage you to apply directly through our website for a smoother application process. It helps us keep track of your application and ensures you’re considered for the role!
How to prepare for a job interview at G-20 Group
✨Know Your Models Inside Out
Make sure you can explain your probabilistic models clearly. Be ready to discuss how you would combine different signals and evaluate their effectiveness. Practising explaining complex concepts in simple terms will help you communicate effectively during the interview.
✨Show Off Your Engineering Skills
Since this role requires strong engineering skills, be prepared to talk about your experience with Python and any production systems you've worked on. Bring examples of data pipelines or real-time services you've built, and be ready to discuss how you ensure reproducibility and robustness in your work.
✨Understand Market Mechanics
Familiarise yourself with prediction market mechanics and trading concepts. Be ready to discuss how you would identify mis-pricings and design arbitrage strategies. Showing that you understand the nuances of market microstructure will set you apart from other candidates.
✨Prepare for Technical Questions
Expect technical questions related to statistics, probability, and machine learning. Brush up on evaluation metrics like log loss and Brier score, and be ready to discuss your experience with backtesting predictive models. This will demonstrate your solid foundation in the quantitative aspects of the role.
