Build high-performing analytics teams and deliver smarter product pricing decisions for the Auto line within the Personal Insurance Loss and Demand Modeling team.
Locations (4)
Columbus, Ohio, United States
Chicago, Illinois, United States
Hartford, Connecticut, United States
Charlotte, North Carolina, United States
Compensation
$81,040 - $121,560
Responsibilities
Develop and evaluate predictive modeling solutions that support the personal auto class plan, unconstrained loss models, and customer demand models.
Evaluate new data sources and modeling approaches to boost product performance. Engineer features to uncover additional signal for our models.
Support model implementations with state Departments of Insurance (DOI), serving as a subject matter expert on the models you build and responding to DOI queries.
Maintain a portfolio of repositories and scripts and suggest improvements to workflows. Assist in the modernization of model deployments using our cloud-based MLOps tools.
Collaborate with team members to plan and deliver projects in an Agile framework.
Requirements
Master's degree in Statistics, Data Science, Mathematics, Actuarial Science, Economics, Engineering, Physics, or similar analytical field
Nice to have: experience in the insurance industry
We are looking for a Project Lead familiar with cloud-native and AI-native development practices to lead a team of engineers working with LLMs, agents, and retrieval systems.
Responsibilities
Develop intuitive AI-native software that meets and exceeds the needs of the company
Development of technical specifications and planning, including model selection, prompt/agent design, and data/retrieval architecture
Analyze and automate tests for software components prior to delivery, and build evaluation harnesses (accuracy, regression, and hallucination checks) for AI features
Troubleshoot coding and model-behavior problems quickly and efficiently to ensure a productive workplace
Follow secure software coding practices, including AI-specific safeguards (prompt-injection defense, data privacy, PII handling, and responsible-AI guardrails)
As a Technical Account Manager (TAM) in AWS Enterprise Support you will play a crucial role in fostering our customers' innovative and transformative endeavors across various technologies including Generative AI/ML, Compute, Storage, Database, Big Data, Application-level Services, Networking, Serverless, Deployment, Security, and more.
Responsibilities
Act as a single point of contact for Enterprise Support customers building trusted advisor relationships
Provide strategic technical guidance across the full cloud adoption lifecycle - from migration planning to operational optimization
Make recommendations on how new AWS offerings (including Generative AI/ML) fit into customer strategy and architecture
Complete analysis and present periodic reviews of operational performance to customers
Provide detailed reviews of service disruptions metrics and prelaunch planning
Requirements
Bachelor's degree
Nice to have: Experience with AWS services or other cloud offerings
Nice to have: Experience in internal enterprise or external customer-facing environment as a technical lead
Building and deploying models that directly power PayPay products, with secondary responsibility for experimentation and data-driven insights.
Compensation
Annual salary paid in 12 installments (monthly). Based on skills, experience, and abilities. Reviewed once a year.
Responsibilities
Own end-to-end design, implementation, evaluation, and maintenance of machine learning models for prediction, recommendation, anti-fraud, etc. from problem framing to production
Lead architectural decisions for data science systems.
Process, analyze, and visualize user and merchant data, providing data-driven insights that influence product strategy for technical and business divisions.
Collaborate with data engineers, product managers, and stakeholders to build robust production systems
Requirements
Bachelors in a quantitative field such as Computer Science, Machine Learning, Mathematics, Statistics, Economics, Physics, or equivalent
Nice to have: Masters or PhD in a quantitative field
Lead automated testing and MLOps quality for Hivemind autonomy platform.
Compensation
Pay within range listed + Bonus + Benefits + Equity
Responsibilities
Own technical strategy, architecture, and roadmap for automated testing, verification, and MLOps quality across the Hivemind ecosystem.
Design and maintain scalable test frameworks for autonomy software, backend services, APIs, operator-facing applications, and distributed hardware environments.
Lead functional, integration, regression, system, performance, reliability, and end-to-end testing across simulation, edge-compute, software-in-the-loop, and hardware-in-the-loop environments.
Build automated ML validation pipelines covering data quality, training reproducibility, model accuracy, robustness, regression, latency, resource utilization, and system integration.
Establish CI/CD and continuous training workflows that provide versioning and traceability for datasets, models, configurations, evaluation results, and deployment artifacts.
Requirements
Bachelor's degree in Computer Science or related field
Data Science InternshipNewSonio · Paris, France
Staff Software EngineerNewPlume · Ljubljana, Slovenia
Cloud Security LeadNewEricsson · Cairo, Egypt
Technical Account Manager, CMHK Enterprise SupportNewAmazon · Shenzhen, People's Republic of China
Senior Data Science Engineer / Senior Machine Learning Engineer (Applied Insights)NewPayPay · Japan
Staff Engineer, Test Automationf7d8749a-72d6-407c-bf98-292ea630c73a · San Mateo, US