5/6/2026

Drank a cup of coffee; Made snarky remark to HN user.

If you own a house or building in Florida and have a mortgage, you're required to carry it. Here's how a policy gets priced:

You go to a retail broker with your info. They pass it to a wholesaler, who puts the submission out into the market for quotes. Any carrier or MGA that wants the business prices the CAT and AOP (non-CAT) portions separately. Actuaries build models for the AOP side, while Verisk and Moody's model the CAT portion. Those two numbers get added together, plus some fees — and that's your annual premium.

From there, the insurers buy reinsurance on their portfolios. The reinsurers run those same models, do their magic, and come up with their own price.

Just an example, because no major hurricanes have hit the south east in a while, premiums are down 30% right now. All of the insurance companies are getting squeezed.

https://www.nbcmiami.com/investigations/miami-beach-resilien...

Responsibilities: Design, train, fine-tune, and evaluate ML models (LLMs, classification, sequence models) for property insurance. Build and maintain robust data pipelines that feed training, evaluation, and inference workloads at scale. Develop rigorous evaluation frameworks — establish metrics, build rater alignment processes, and apply statistical methods to determine when a candidate model is genuinely better than production. Run controlled experiments, ablations, and A/B tests; communicate findings clearly with appropriate uncertainty quantification. Deploy models to production and own their performance, drift monitoring, and iteration cycles. Collaborate with the engineering team to integrate ML services into our backend (Django/Python) and frontend (React/TypeScript) products. Stay current with the ML literature and translate relevant advances into practical improvements for our products.

Required:

PhD in Statistics, Machine Learning, Computer Science, Applied Mathematics, or a closely related quantitative field (or equivalent research experience with a strong publication or production track record). Strong foundation in statistics — experimental design, hypothesis testing, Bayesian methods, and uncertainty quantification. Minimum 5 years of combined research and applied ML experience, with a proven track record of shipping models to production. Deep proficiency in Python and the modern ML stack (PyTorch, Hugging Face, scikit-learn, pandas, NumPy). Hands-on experience with LLMs, including fine-tuning (LoRA/QLoRA, full fine-tunes), prompt engineering, and evaluation. Strong data engineering skills — comfort building reliable pipelines over messy real-world data, working with SQL and columnar formats. Excellent debugging, problem-solving, and written communication skills.

Why Join Ping: Work directly on ML systems that touch real production traffic from day one. Collaborate with a small, senior team of insurance and tech veterans building products that are reshaping the property insurance industry. Enjoy the autonomy of a research role with the impact of an applied one — your models will be in production, used by real customers, and you will see the results immediately. Please apply at jobs@pingintel.com

is anyone else seeing this / fixed this problem ?

Ping Data Intelligence is a dynamic startup based in Miami, FL, revolutionizing the property insurance sector with cutting-edge web technologies and ML-powered tools. Despite rapid growth, we retain the stability of a self-funded, profitable company. Role Overview: As a Senior Software Engineer at Ping, you will work onsite in Delhi, India, sitting alongside our data entry team to increase efficiency, streamline processes through ML and UI improvements, and triage and fix bugs that occur overnight (US time). You will have a direct, daily impact on production quality and team productivity. Occasional work from home is available. Responsibilities:

Work closely with the data entry team to identify bottlenecks, streamline workflows, and build tooling that increases throughput and accuracy. Develop and implement ML-driven and UI-based improvements to internal processes. Triage and resolve production bugs that arise overnight, ensuring smooth operations for US-based customers each morning. Collaborate with the US-based engineering team to integrate APIs, optimize data flow, and ship frontend and backend improvements. Oversee projects from problem identification through deployment, including architecting solutions, running experiments, and managing rollouts.

Required:

Minimum 5 years of experience in software development. Strong proficiency in React, TypeScript, and modern frontend practices. Comfort working across the stack — willingness to dig into backend (Python/Django) and ML pipelines as needed. Proven track record of deploying and maintaining production web applications. Strong debugging and problem-solving skills, especially under time pressure.

Preferable:

Experience with machine learning workflows or data processing pipelines. Familiarity with Python/Django backend development. Knowledge of cloud services (AWS) and containerization (Docker). Experience in insurance, fintech, or data-intensive industries.

Tech Stack: React, TypeScript, Python, Django, SCSS, Redux Toolkit, Jest, Webpack, Node.js, Git, AWS. Why Join Ping: Be a key player with direct influence on how our team operates day to day. Work with a top-tier team of insurance and tech veterans building ML-powered products that are transforming the property insurance industry. Your work will have immediate, visible impact on production quality and team efficiency. Please apply at jobs@pingintel.com