Life & Annuity Specialist

What you would do

• Create realistic scenarios involving needs analysis, illustration preparation, underwriting decisions, and policy replacements
• Write expert-level reference responses demonstrating appropriate suitability reasoning and regulatory compliance
• Evaluate AI-generated responses for product accuracy, mathematical correctness, tax treatment, and disclosure adequacy
• Identify unsuitable recommendations, confusion between product types, and failures to apply regulatory requirements
• Provide written feedback guiding the research team toward model improvement

Who they want

• 2+ years professional experience in life insurance advisory, underwriting, new business, policy administration, or suitability review
• Proficiency with several product categories including mortality insurance, indexed life contracts, and distribution vehicles
• Understanding of policy mechanics including cost-of-insurance, crediting rates, caps, participation rates, and surrender charges
• Ability to explain tax treatment and suitability considerations clearly; recognition of when licensed or legal counsel is needed
• Excellent written communication, quantitative accuracy, and meticulous attention to detail; minimum 20 hours weekly

What the interview asks about

1. 1.Understanding policy mechanics

   AI errors in calculating outcomes stem from misunderstanding how product features interact, so your ability to trace through mechanics catches fundamental model failures.

   For example: “An indexed universal life policy has a 2 percent floor, 15 percent cap, and 80 percent participation rate in S&P performance. Explain how you'd verify the AI calculated annual crediting correctly for a year the index rose 20 percent.”

2. 2.Identifying suitability failures

   Regulatory requirements protect consumers, so training AI to spot unsuitable recommendations is critical for responsible AI deployment in financial services.

   For example: “An AI-generated response recommends a variable annuity with 2 percent charges for a 78-year-old with minimal liquid assets and no investment experience. What suitability problems would you flag?”

3. 3.Designing realistic advisor scenarios

   Effective training data must reflect actual practice; contrived scenarios produce models that fail in real advisory conversations and underwriting situations.

   For example: “Describe a realistic needs-analysis scenario where a 45-year-old married professional with a 10-year-old child and a pension might need a buy-sell agreement structure. Include specific financial details.”

4. 4.Explaining tax and regulatory logic

   Your written feedback educates the research team about domain rules, turning your domain expertise into actionable guidance for model refinement.

   For example: “The AI incorrectly applied Regulation Best Interest disclosure requirements to an insurance recommendation. Explain in detail what the AI missed and why this matters for the model's training.”

A task you may get

Design a realistic life insurance scenario with medical underwriting, create an expert response demonstrating sound suitability reasoning, then review an AI response and identify at least two substantive errors.

How to prepare

• Review your state's replacement and disclosure rules, Reg BI guidance, and current policy illustration standards
• Refresh your knowledge of tax treatment for life insurance, annuities, and section 1035 exchanges
• Study examples of product confusion and suitability failures to understand common model errors
• Prepare case studies showing how policy mechanics drive different outcomes across product types

The facts

Pay

$80/hr

Hours

Hourly, 20 hours a week

Where

Remote · Remote United States, Canada, or United Kingdom

Open to

USA, CAN, GBR, AUS, NZL, IRL, MLT, NLD, SWE, DNK, NOR, FIN, DEU, AUT, BEL

Field

Finance

Posted

8/28/2026

Places left

1

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