About Time Seventeen
We give risk a price. We create state prices for tail events.
The world contains a large class of risks that determine where capital flows yet have no price today. Global supply chains are being reconfigured, while the geopolitical discount rates that inform consequential decisions remain difficult to quantify. Time Seventeen is building tradable synthetic indices and term structures for these risks.
Time Seventeen is infrastructure for geopolitical risk and catastrophe risk. We give risk a price. We create state prices for tail events. Our ambition is to turn fragmented judgments about individual events into continuous risk prices, then bring those prices into institutional valuation, trading, and risk-transfer workflows.
Our work connects event definitions, probabilistic forecasting, market data, index methodology, and execution infrastructure. We explore how prediction markets, actuarial models, insurance and reinsurance structures, and blockchain-based DeFi can contribute to a common language for uncertain outcomes. The challenge is to make risk legible across different horizons, exposures, and market participants, while keeping assumptions and limitations explicit.
Building this infrastructure requires more than a compelling forecast. Data must be traceable, events must have defensible resolution rules, methodologies must survive scrutiny, and products must account for liquidity, basis risk, operational resilience, and applicable legal constraints. We want institutions to be able to understand a risk price, challenge its construction, and assess how it relates to the exposure they actually hold.
We are building a focused team across research, engineering, markets, and risk. We value people who can move between deep specialist work and the practical decisions needed to build a company: identifying the important question, testing an approach, communicating the evidence, and carrying the result into a usable system.
About the role
Support prediction-market research and engineering through event analysis, market-data tooling, and reproducible experiments.
You will work on a defined project with mentorship from the prediction-market team. The internship offers practical exposure to event contracts, probabilistic reasoning, market microstructure, and the systems that connect market signals to event-risk indices.
Depending on your strengths, your project may emphasize data integration, market-quality analysis, event-resolution research, or simulation. We expect careful work and clear communication; prior professional prediction-market experience is not required.
What you'll do
- Research event definitions, authoritative sources, resolution rules, and potential ambiguity for proposed markets.
- Collect and clean market metadata, price histories, order-book snapshots, and outcome data with traceable sources.
- Analyze spreads, depth, stale prices, and basic probability or calibration measures with guidance from the team.
- Build small scripts, notebooks, dashboards, or replay utilities with documented setup and reproducible outputs.
- Compare event contracts and settlement approaches and identify differences that affect comparability or index use.
- Help test data adapters and investigate missing, inconsistent, or duplicate records.
- Present findings, limitations, and next steps in concise research notes and project reviews.
What we're looking for
- Student or early-career candidate in computer science, mathematics, statistics, economics, finance, engineering, or a related field.
- Foundational probability and statistics knowledge and curiosity about how prediction markets form prices.
- Ability to use Python, SQL, or comparable analytical tools; basic Git and API skills are helpful.
- Evidence of a coding or analytical project that you can explain, including your own contribution.
- Careful source evaluation, attention to detail, and willingness to question assumptions.
- Clear written communication, reliable follow-through, and openness to feedback.
- Interest in prediction markets, DeFi, geopolitical risk, or catastrophe-risk pricing; domain experience is welcome but not required.
What success looks like
- Complete a scoped project with a reproducible dataset, tool, or analytical report.
- Explain the relevant market mechanics, source limitations, and implications of the findings.
- Leave clear documentation and a useful handoff for colleagues to extend the work.
Location & working arrangement
This role can be based in San Francisco (SFO) or Hong Kong (HK). Please include your preferred office, current location, time zone, start date, and any working-arrangement requirements. You will collaborate with colleagues across both locations.
How to apply
Share your CV and a relevant project, code repository, market analysis, or technical case study. Explain your personal contribution, design decisions, validation method, and lessons learned. Please exclude confidential information belonging to employers or clients. Include your preferred internship dates, duration, weekly availability, and academic commitments.
How we work
Time Seventeen is a focused, high-ownership team. We value clear thinking, direct communication, careful execution, and people who carry important work through to a result. We expect colleagues to surface inconvenient evidence, ask precise questions, and make the limits of their work explicit. Strong specialist judgment matters, as does the willingness to understand how your work affects research, markets, engineering, and risk. We prefer useful documentation and clear decisions to unnecessary process.