Actuary

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

Apply actuarial reasoning to uncertain events, helping us design calibrated models and decision tools for complex financial risks.

You will help connect probabilistic event models to the economics of insurance and reinsurance. Frequency, severity, dependence, and tail uncertainty need to be understandable both to modelers and to the people evaluating risk-transfer structures. You will examine what an event-linked price does and does not imply about an underlying loss exposure.

You will work with insurance, index, and data colleagues to select suitable methods, validate inputs, and explain how results relate to a defined exposure. Projects may include catastrophe scenarios, parametric triggers, or loss distributions for emerging risks. The role requires careful separation of model estimates, market-implied probabilities, and contractual payout terms.

What you'll do

• Develop frequency, severity, survival, and scenario models for emerging risks.
• Assess assumptions, uncertainty, tail behavior, and model sensitivity.
• Translate actuarial methods into reusable analytical and product capabilities.
• Communicate results and limitations clearly to technical and commercial stakeholders.
• Evaluate exposure-to-index mapping, basis risk, dependence, and tail sensitivity when assessing proposed insurance, reinsurance, or event-linked structures.
• Validate exposure and loss inputs, compare results with available experience or benchmarks, and maintain reproducible calculations and model-change records.

What we're looking for

• Actuarial training with progress toward or completion of a recognized qualification.
• Strong grounding in probability, statistics, financial mathematics, and risk modeling.
• Experience using Python, R, SQL, or similar analytical tools.
• Interest in prediction markets, digital assets, or novel forms of risk transfer.
• Experience validating actuarial models and explaining how sparse data, dependence, parameter uncertainty, and changing exposures affect estimates.
• Ability to interpret insurance or reinsurance structures and communicate modeling results to both quantitative colleagues and commercial decision makers.

What success looks like

• Deliver a reviewed model or exposure analysis with traceable inputs, reproducible calculations, and documented validation.
• Quantify material sensitivities and explain the basis risk between the proposed index or trigger and the underlying loss exposure.
• Provide an actionable actuarial recommendation with clear limitations and requirements for further data or specialist review.

Location & working arrangement

This role is based in Hong Kong. Please include your location, availability, and any working-arrangement requirements in your application.

How to apply

Share your CV and describe an actuarial model or exposure analysis, including validation and material sensitivities. Explain your personal contribution, important decisions, and results. A relevant work sample is welcome where you have permission to share it; please exclude confidential employer or client information. Include your availability and why Time Seventeen's work interests you.

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.