Risk & Actuarial AI Expert

Job not on LinkedIn

🕒 April 21

🇺🇸 United States – Remote

💵 $100 - $120 / hour

⏱ Part Time

🟢 Junior

🟡 Mid-level

📊 Actuary

👻 Ghost score 55%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Weekday (YC W21)

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2021

💼 Consulting

👥 HR Tech

☁️ SaaS

Consulting • HR Tech • SaaS

Weekday is a modern recruitment platform that combines AI technologies with a vast database of potential candidates, aiming to streamline the hiring process for companies in India. They offer various services, including a proactive outreach approach that helps employers connect with top talent, as well as tools for candidates to easily apply for jobs. Weekday's emphasis on candidate engagement through multiple channels, including email, WhatsApp, and phone calls, sets it apart in the competitive landscape of recruitment agencies.

📋 Description

• Evaluate and optimize portfolio performance through detailed loss ratio and combined ratio analysis • Conduct comprehensive portfolio risk assessments using statistical models and AI-driven techniques • Focus on catastrophe modeling and exposure management, enhancing traditional modeling using machine learning techniques • Leverage AI/ML techniques to automate actuarial workflows and improve predictive modeling

🎯 Requirements

• Bachelor’s or Master’s degree in Actuarial Science, Mathematics, Statistics, Data Science, or a related field • Progress toward actuarial certification (e.g., IFoA, SOA, or equivalent) preferred • 2–8 years of experience in actuarial analysis, risk management, or insurance analytics • Strong expertise in loss ratio and combined ratio analysis • Proven experience in portfolio risk assessment and risk modeling • Hands-on experience with catastrophe modeling tools and exposure management frameworks • Proficiency in programming (Python/R) and data visualization tools • Familiarity with machine learning techniques and their application in insurance • Strong problem-solving skills and ability to communicate complex insights to non-technical stakeholders.

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