Insurance & Actuarial Intern

Time Seventeen; Hong Kong; On-site; HKD 180,000 - 280,000; Full time

Job summary

Time Seventeen is seeking an early-career researcher to evaluate event-linked risk prices for catastrophe and geopolitical exposures in Hong Kong. You will work with actuarial and market colleagues to analyze exposure, policy triggers, dependence, and data limitations, bridging research and practical insurance questions.

The role emphasizes transparent analysis, reproducible research materials, and collaboration across research, markets, and risk teams in a focused environment.

Qualifications

• Strong foundation in probability and statistics with interest in tail-risk modeling.
• Proficient using Excel, Python, or R for analysis.
• Actuarial exam progress or insurance coursework is helpful but not required.

Responsibilities

• Research insurance, reinsurance, catastrophe-bond, and parametric risk-transfer structures.
• Support frequency, severity, exposure, and scenario analysis using available datasets.
• Examine policy triggers and event-resolution rules, including exclusions and potential ambiguity.
• Analyze basis risk and sensitivity when mapping an index to an insured exposure.
• Document data provenance, modeling assumptions, dependence, and uncertainty.
• Prepare clear research notes for actuarial, commercial, and product discussions.

Skills

Excel

Python

R

Probability

Statistics

Education

Actuarial science

Statistics

Finance

Actuarial exam progress

Job description

About Time Seventeen

We give risk a price. We create state prices for tail events.

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 insurance and reinsurance applications for event-linked risk prices and support actuarial analysis of catastrophe and geopolitical exposures.

You will explore how risk indices and probabilistic models relate to the losses that insurers, reinsurers, and institutional clients face. The work includes understanding exposure, policy triggers, event definitions, dependence, and the basis risk between an index and an actual claim.

Working with research and market colleagues, you will support well-scoped analytical projects and document their limitations. You may review risk-transfer structures, analyze frequency and severity data, or compare scenarios under different assumptions. The aim is to build a sound bridge between event-risk information and practical insurance questions.

What you'll do

• Research insurance, reinsurance, catastrophe-bond, and parametric risk-transfer structures.
• Support frequency, severity, exposure, and scenario analysis using available datasets.
• Examine policy triggers and event-resolution rules, including exclusions and potential ambiguity.
• Analyze basis risk and sensitivity when mapping an index to an insured exposure.
• Document data provenance, modeling assumptions, dependence, and uncertainty.
• Prepare clear research notes for actuarial, commercial, and product discussions.

What we're looking for

• Student or early-career candidate in actuarial science, insurance, statistics, mathematics, or finance.
• A foundation in probability and statistics and an interest in tail-risk modeling.
• Working knowledge of Excel, Python, R, or another analytical tool.
• Careful reasoning about incomplete data and model limitations.
• Actuarial exam progress or insurance-related coursework is helpful but not required.

What success looks like

• Deliver a transparent analysis of a defined exposure or risk-transfer use case.
• Identify important assumptions, basis risks, and data limitations.
• Create reusable research materials for the insurance and actuarial roadmap.

Location & working arrangement

This role is based in Hong Kong. Please share your availability, current location, and any academic or scheduling requirements.

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.