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Subject Matter Expert – Insurance Policy Renewal & Retention (AI Evaluation Project) Bilingual: German / English Engagement Type Independent Contractor (1099) Fully Remote (United States-based candidates only) Estimated Duration: 1–3 months This is a contract role and does not create an employer–employee relationship. Contractors are responsible for their own taxes. Role Overview We are seeking an experienced Insurance Subject Matter Expert (SME) to support an AI training and model development initiative focused on insurance policy renewal and retention workflows. In this role, you will evaluate AI-generated outputs, provide domain expertise, and help improve model accuracy across insurance-related use cases. Key Responsibilities Collaborate with AI, data science, and product teams to evaluate AI models used in underwriting, claims processing, risk assessment, pricing, policy renewal, and retention Identify gaps, biases, and limitations in AI-generated outputs and provide domain-driven recommendations for improvement Validate AI outputs against real-world insurance operations, regulatory requirements, and customer experience standards Define key performance metrics and success criteria for model evaluation Support data labeling and annotation by providing insurance-specific expertise and context Contribute to the design of explainable AI frameworks to improve transparency and trustworthiness Stay updated on insurance regulations, compliance standards, and emerging AI applications in the insurance sector Required Qualifications Bachelor’s or Master’s degree in Insurance, Risk Management, Actuarial Science, Business, or a related field 7+ years of experience in the insurance industry, including areas such as: Underwriting Claims processing Risk modeling Pricing / quoting / binding Policy renewal and retention Experience working with Large Language Models (LLMs) or AI-driven systems preferred Experience in client consultation, needs analysis, or product management Background in insurance organizations such as carriers, reinsurers, brokerage/advisory firms, or insurtech companies Strong understanding of insurance operations, policy structures, and regulatory frameworks Experience working in cross-functional teams (AI, product, engineering, analytics) Bilingual proficiency in German and English (required) Additional Information Strong analytical thinking and attention to detail required Ability to translate domain knowledge into structured AI evaluation feedback Must be able to work independently in a fast-paced, remote environment
Project ID: 40420863
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Active 10 days ago
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Hi VisaraPartners, Last week I completed a similar bilingual (DE/EN) renewal & retention AI eval, so I can handle this well. I’m US‑based with 10+ yrs in underwriting, pricing, claims, renewals, and hands‑on LLM evaluation. My Questions. 1) What model stack and eval tools are you using (OpenAI/Anthropic/Azure, RAG, TruLens/W&B/custom), and which KPIs matter most? 2) What data/privacy limits apply (US-only processing, PII/PHI rules), and is German–English output parity required? My Suggestions. 1) Use a weighted, policy‑based rubric (accuracy, compliance, CX, explainability) and automate eval on stratified scenarios. 2) Build a bilingual test set with edge cases and key regulatory variants to reduce bias and drift. My Execution Plan. - Align scope: workflows, jurisdictions, SLAs. - Audit models, prompts, data, metrics; find gaps. - Define rubric and gold labels; craft DE/EN scenarios. - Run baseline evals; error analysis by use case. - Improve: prompts, guardrails, retrieval, policy rules. - Validate via A/B and red teaming; verify compliance and CX. - Document; deliver dashboards and playbooks. - Support deployment review and post‑release monitoring.
$51 USD in 40 days
5.8
5.8

With over a decade of experience in full-stack architecture and high-scale systems, I understand the critical need for a Bilingual Insurance Renewal & Retention AI Expert in supporting your AI training and model development project. My background in high-complexity systems, such as scaling for 1M+ users and working in FinTech, directly applies to the challenges of evaluating AI models for insurance policy workflows. To ensure scalability and accuracy in AI model development, I recommend implementing a robust data labeling process combined with domain-specific expertise for validation. In a previous project, I successfully scaled Telegram Mini Apps to serve over 1 million users, showcasing my ability to handle complex systems effectively. I invite you to connect with me to discuss how we can collaborate on improving model accuracy, addressing biases, and enhancing transparency in AI frameworks for insurance operations. Let's work together to ensure the success of your AI evaluation initiative.
$50 USD in 15 days
2.5
2.5

Models for renewal and retention usually trip over clause-level logic and noisy churn signals — doubly true when you need bilingual (German/English) coverage. I often see AI pass global tests but fail on mid-term endorsements, cancellation windows, and regulatory wording. I'd start by building a concise bilingual annotation guide mapping policy language to renewal outcomes, then label a focused sample (200–500 cases) in Labelbox or Prodigy to get a signal fast. I’ll define KPIs (precision/recall for renewal intent, calibration error for pricing), run targeted error analysis, and add rule-based overrides for regulatory edge cases plus SHAP/LIME explainability for any pricing/score models. I can also produce reviewer instructions so non-experts can continue labeling. Quick question: do you want the initial validation focused on personal lines or commercial lines?
$50 USD in 7 days
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Bengaluru, India
Member since Mar 20, 2026
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