Quantitative Intern

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
Research prediction-market probabilities, calibration, event resolution, and price discovery in Hong Kong beginning May 2027. You will work with researchers and engineers to turn a well-defined question into a dataset, a testable hypothesis, and an analysis that colleagues can review. The work connects prediction-market prices, event outcomes, and external risk indicators to the construction and evaluation of synthetic risk indices and term structures. Projects focus on prediction-market research: evaluating probability calibration, studying liquidity and price discovery, comparing event definitions and resolution rules, and testing how market-implied probabilities can inform event-risk indices. You will receive guidance on project scope and review while owning the quality of your data, code, and conclusions. Methods should suit the question and available evidence, with explicit baselines and uncertainty estimates. This internship is suited to candidates who enjoy both mathematics and implementation. You should be comfortable investigating imperfect data, revising a hypothesis when results contradict it, and explaining uncertainty. Prior professional trading experience is not required; coursework, independent research, and practical projects can demonstrate the relevant skills.

What you'll do
• Collect, clean, and align historical prices, event metadata, outcomes, and relevant external datasets; document source provenance, timestamps, missing values, and transformations.
• Formulate research hypotheses with explicit assumptions, measurable evaluation criteria, and simple baselines before introducing more complex models.
• Analyze probability calibration, forecast accuracy, dependence, and tail scenarios using statistical methods appropriate to the available sample and event structure.
• Build reproducible Python research pipelines and backtests with chronological train/test splits, leakage checks, and careful handling of event resolution and changing market availability.
• Evaluate proposed signals and index methods against transaction costs, spreads, liquidity, turnover, and execution assumptions; distinguish statistical evidence from feasible market performance.
• Run sensitivity analyses and robustness checks across time periods, parameters, and datasets, and investigate whether results depend on a small number of observations.
• Collaborate with engineering and market colleagues to validate data definitions and translate useful findings into reviewed analytical tools or research inputs.
• Write clear research notes and present methods, results, limitations, and next steps; maintain versioned code and a handoff that another researcher can reproduce.
• Create and assess candidate features using only information available at the relevant observation time; account for repeated experimentation and selection bias when interpreting results. What we're looking for
• Currently studying, or recently graduated in, mathematics, statistics, computer science, physics, engineering, econometrics, quantitative finance, or a related discipline. Evidence from rigorous independent study and projects is also welcome.
• A strong foundation in probability, conditional expectation, distributions, estimation, hypothesis testing, regression, and the interpretation of uncertainty; explain the assumptions and limitations of the methods you use.
• Comfort with linear algebra, calculus, numerical methods, and basic optimization, including checking units, numerical stability, and whether a result is economically and statistically plausible.
• Practical Python proficiency, including NumPy and pandas, and the ability to write readable, modular code for data processing, analysis, and experiments. Submit a project that demonstrates your own implementation and debugging work.
• Working knowledge of data structures and algorithms, Git, and automated testing. Be able to document dependencies, reproduce an experiment from a clean environment, and explain design and complexity tradeoffs.
• Ability to query and validate structured datasets, align timestamps and event identifiers, handle missing or duplicate observations, and document source provenance. SQL and Linux experience are helpful.
• Understanding of overfitting, chronological train/test splits, look-ahead bias, leakage, selection bias, and repeated experimentation. Evaluate a proposed method against simple baselines and meaningful out-of-sample tests.
• Interest in prediction markets and market microstructure: event contracts, resolution rules, probabilities, order books, spreads, liquidity, fees, and settlement. Prior professional trading experience is not required.
• Ability to distinguish quoted prices from executable prices, account for transaction costs and liquidity, and state where a backtest or simulator relies on assumptions that may not hold in practice.
• Evidence of a substantive quantitative or software project, from coursework, independent research, an internship, or open source. Explain the question, your personal contribution, validation, limitations, and what you would improve.
• Clear written and spoken English, careful research notes, and the ability to communicate technical findings and uncertainty to colleagues across research and engineering. Incorporate review feedback and report negative results honestly.
• Available to work from Hong Kong during the agreed internship period. Share your current location, academic commitments, weekly availability, and relevant work-authorization or sponsorship requirements; eligibility will be discussed individually.
• For prediction-market research, be comfortable evaluating calibration, forecast accuracy, dependence, and event definitions, and selecting evaluation methods appropriate to resolved and unresolved events.
• Time-series analysis, Bayesian methods, econometrics, Monte Carlo simulation, or work with financial or event datasets are useful. Explain when sample size, changing market coverage, or ambiguous resolution limits a conclusion. What success looks like
• Complete a scoped research project with a clear question, documented dataset, reproducible code, and a report that explains the evidence and limitations.
• Compare the proposed approach with credible baselines and demonstrate appropriate out-of-sample evaluation and sensitivity checks.
• Identify a useful methodological finding, data-quality improvement, or research direction, including a well-supported negative result when an approach does not hold up.
• Leave reviewed code and documentation that colleagues can rerun and extend, with practical next steps for research or implementation. Location & working arrangement This internship is based in Hong Kong and begins in May 2027. Please share your exact available start and end dates, weekly availability, current location, academic commitments, and work-authorization or sponsorship requirements. Any scheduling constraints will be discussed during the interview process.

How to apply
Submit your CV and, where available, a research report, notebook, code repository, or quantitative project. Describe the research question, your personal contribution, sources and methods, chronological evaluation, robustness checks, and limitations. Coursework and independent projects are welcome. Confirm your availability in Hong Kong from May 2027, including exact dates and weekly commitments. Exclude confidential employer, client, or university research information. 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. Apply for this role→