Quant Intern

Time Seventeen; Hong Kong; In loco; HKD 167.000 - 246.000; Tempo pieno

Descrizione del lavoro

Time Seventeen is seeking an internship for a quantitative student or recent graduate to join our research team in Hong Kong. You will work with researchers and engineers to turn questions into datasets, form testable hypotheses, and deliver analysis that can be reviewed by colleagues.

The role requires comfort with Python, NumPy, and pandas, plus a solid foundation in probability and statistics. Coursework or projects demonstrating practical data handling and clear uncertainty communication are

Competenze

• Currently studying, or recently graduated in mathematics, statistics, computer science, physics, engineering, econometrics, quantitative finance, or a related quantitative discipline.
• Solid foundation in probability, statistics, linear algebra, and calculus with ability to explain analytical assumptions.
• Working proficiency in Python and experience with NumPy, pandas, and at least one statistical/modeling library.
• Familiarity with Git and reproducible notebooks or scripts.
• Understanding of hypothesis testing, regression, data leakage in time-dependent research.
• Interest in market microstructure, risk pricing, and geopolitical/catastrophe events.
• Clear written and spoken English with attention to detail.
• SQL, time-series analysis, Monte Carlo simulation, ML are helpful but not required.

Mansioni

• Collect, clean, and align historical prices, event metadata, outcomes, and external datasets; document provenance and transformations.
• Formulate research hypotheses with explicit assumptions and clear evaluation criteria.
• Analyze calibration, forecast accuracy, dependence, and tail scenarios using appropriate statistical methods.
• Build reproducible Python research pipelines and backtests with proper data handling.
• Evaluate signals and index methods against costs, liquidity, and turnover; differentiate evidence from market performance.
• Run sensitivity analyses across time, parameters, and datasets; check robustness to outliers.
• Collaborate with engineering and market colleagues to validate data definitions and translate findings into tools.
• Write research notes and present methods, results, and limitations; maintain versioned code.
• Create candidate features using information available at observation time; account for selection bias.

Conoscenze

Python programming

Statistics

Probability

Data analysis

English communication

Git

Team collaboration

Formazione

Mathematics/Statistics/CS degree

Strumenti

NumPy

pandas

Descrizione del lavoro

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 Time Seventeen

We give risk a price. We create state prices for tail events. We develop reproducible quantitative research on event probabilities, risk indices, and market behavior to support pricing geopolitical and catastrophe risk. 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 may include evaluating probability calibration, studying liquidity and price discovery, testing an index construction method, or researching a trading signal. 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, from simple statistical baselines to more complex models when justified. 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 quantitative discipline.
• A solid foundation in probability, statistics, linear algebra, and calculus, with the ability to explain the assumptions behind an analytical method.
• Working proficiency in Python and practical experience with NumPy, pandas, and at least one statistical or modeling library through coursework or projects.
• Ability to explore unfamiliar datasets, check data quality, and write readable code; familiarity with Git and reproducible notebooks or scripts.
• Understanding of hypothesis testing, regression, overfitting, and out-of-sample evaluation; awareness of look-ahead bias and data leakage in time-dependent research.
• Interest in market microstructure, prediction markets, risk pricing, and geopolitical or catastrophe events, with a willingness to learn the underlying market mechanics.
• Clear written and spoken English, careful attention to detail, and the ability to explain numerical results and uncertainty to colleagues from different disciplines.
• SQL, time-series analysis, optimization, Monte Carlo simulation, machine learning, or research on financial or event data are helpful but not required.

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 role is based in Hong Kong. Please share your current location, preferred start and end dates, internship duration, weekly availability, time zone, and any academic commitments or work-authorization requirements. The project scope and working schedule will be discussed during the interview process.

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.