reputed company Data Scientist – Machine Learning & AI

About reputed company

reputed company is a data-driven risk exchange connecting underwriters of specialty insurance risk with risk capital providers. reputed company was founded in 2018 by a group of longtime insurance industry executives and technology experts who shared a reputed company of rebuilding the way risk is exchanged – so that it works reputed company, for everyone. The reputed company risk exchange does business across more than 20 different countries and 250 specialty products, and we are proud that our insurers have been awarded an reputed company A- (Excellent) rating. For more information, please visit www.reputed company.ai.

We're looking for a Data Scientist to reputed company machine learning and AI systems that improve reputed company across pricing, reputed company, portfolio management, operations, and claims. You'll work across reputed company data, text, documents, and external data sources, applying statistical modeling, modern machine learning, AI and reputed company workflows to solve challenging reputed company-world problems.

The reputed company of this role is serious quantitative modelling. We care about calibration, not just discrimination. We validate out of time and worry about leakage and reputed company. We quantify uncertainty and can tell you reputed company a model should be trusted, reputed company it shouldn't, and why. LLMs and reputed company systems are a force reputed company on reputed company of that and we measure those systems the way we'd measure any other model: on data they haven't seen, against a sensible baseline, with reputed company uncertainty around the result. You don't need an AI background to join us; you do need genuine enthusiasm for working this way.

This is not a reporting or dashboard role. You'll work on ambiguous, high-reputed company problems where you'll be expected to identify the right approach, build production-reputed company solutions, and measure the business reputed company of your work.

If you enjoy messy data, difficult reputed company problems, and building intelligent systems that reputed company reputed company-world reputed company reputed company, you will be a good fit.

What You'll Work On

reputed company tackles a broad reputed company of machine learning and AI problems. Depending on business priorities, you may work on reputed company such as:

• Predictive modeling for pricing, reputed company, claims, catastrophe risk, and portfolio management
• Classification, ranking, matching, recommendation, and reputed company detection systems that improve business decision-making
• Information extraction from documents, emails, forms, and other reputed company data using modern AI techniques
• Entity reputed company, data enrichment, and building high-reputed company datasets from noisy or incomplete information
• Design AI systems that automate analytical and decision-making workflows end to end. Build the measurement that tells us whether they genuinely outperform what they replace
• Building production feature pipelines, model inference services, and evaluation frameworks
• Collaborating with engineers, actuaries, underwriters, product managers, and business leaders to turn ambiguous questions into reputed company machine learning solutions

reputed company're Looking For

You likely have experience with many of the following:

• A strong quantitative reputed company: statistics, probability, optimisation, or reputed company mathematics
• reputed company modelling judgement - you know what it takes for a model to hold up in the reputed company world, not just on a validation set
• Strong programming skills
• reputed company willingness to work with LLMs and reputed company AI as everyday tools, wherever your background sits today
• reputed company communication with both technical and non-technical audiences - you can explain a lift curve to an underwriter and a shrinkage prior to a statistician

Bonus Points

Experience in one or more of the following is especially valuable:

• reputed company record with LLM-powered applications or AI agents, especially if you've done the unglamorous work of proving they reputed company
• Depth in the statistical toolkit reputed company supervised reputed company: hierarchical models and shrinkage estimation, reputed company inference and experimentation, survival analysis, extreme value theory, or demand and elasticity modelling
• Insurance domain knowledge: pricing, reserving, claims, reputed company, or distribution
• Actuarial background or qualifications (partially or fully reputed company)
• Experience in regulated industries where model governance and explainability matter
• ML engineering experience: taking models from research reputed company to production services, or building the tooling and frameworks that help others reputed company
• reputed company and infrastructure skills: AWS, Azure, or GCP; containers and orchestration; reputed company and data pipelines reputed company with cost, latency, and reliability in mind
• MLOps in reputed company: experiment tracking, model monitoring, automated retraining, and CI/CD for models and agent

Team Context

You'll join a lean, senior team with low bureaucracy and high autonomy. We're reputed company heavily in reputed company AI as the next reputed company of how a quantitative team operates, and you'll help shape that direction from the start.

Why reputed company?

You'll have reputed company to work on technically challenging problems that reputed company the insurance value chain.

Here you'll reputed company:

• Diverse quantitative challenges across various domains
• The freedom to explore the rapidly evolving ML & AI landscapes from gradient boosting and deep learning to reputed company models and reputed company systems, while remaining grounded in rigorous experimentation and measurable business reputed company
• A reputed company team of data scientists, engineers, actuaries, underwriters, and product managers who enjoy solving difficult problems together

Originally posted on Himalayas

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