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 execution algorithms and market microstructure in Hong Kong beginning May 2027, using order-book data, simulation, and realistic transaction costs. You will investigate how a trading decision becomes an executable order strategy in prediction markets. The work centers on order placement, fill probability, queue behavior, adverse selection, and the tradeoff between execution cost and completion risk. Projects may include modeling fills, comparing order-placement or cancellation rules, or building an event-driven execution simulator. Research starts with reproducible historical analysis and controlled simulation; proposed algorithms require review before any live use.

What you'll do
• Analyze spreads, depth, order flow, fees, and liquidity across prediction-market conditions.
• Build event-driven simulations with explicit assumptions about queue position, partial fills, cancellations, and data timing.
• Compare execution rules using transaction costs, fill rates, adverse selection, completion time, and risk exposure.
• Check chronological evaluation, look-ahead bias, and the difference between displayed liquidity and executable liquidity.
• Evaluate robustness across markets, event horizons, and liquidity regimes, including unsuccessful strategies.
• Document model assumptions and failure cases and work with engineers on reproducible research tools and safeguards.
• Define execution objectives and constraints before comparing algorithms, including inventory or exposure limits, participation assumptions, and cancellation or completion rules.
• Reconcile simulated orders and fills against observable market events; quantify where available data cannot identify queue position or executable liquidity.
• Assess sensitivity to fees, latency, fill assumptions, parameters, and market regimes, and report uncertainty rather than a single headline performance number.
• Work with reviewers on documented risk limits, reconciliation, and failure behavior; any live use requires separate review and authorization. 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 execution and algorithm research, explain order types, partial fills, queue position, cancellation behavior, adverse selection, market impact, and the tradeoff between completion risk and execution cost.
• Optimization, stochastic processes, event-driven simulation, or transaction-cost analysis are helpful. Be able to identify optimistic fill assumptions and assess sensitivity to latency, fees, and changing liquidity. What success looks like
• Deliver a reproducible execution study or simulator with documented data and fill assumptions.
• Compare candidate algorithms against credible baselines with realistic costs and sensitivity checks.
• Explain the evidence, limitations, and controls needed before any proposed strategy could be considered for live use. 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
Share your CV and an execution study, simulator, backtest, or quantitative project where available. Explain your question, personal contribution, event and fill model, costs, chronological evaluation, sensitivity checks, and limitations. Coursework and independent projects are welcome. Confirm your availability in Hong Kong from May 2027, with exact dates and weekly commitments. Exclude confidential employer, client, or 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→