Extend

Data Scientist

• Own the model lifecycle: requirements, experimentation, model development, evaluation, and model cards, partnering with ML engineers on deployment and production infrastructure
• Translate complex fraud patterns into well-framed ML solutions: defining what to model, what success looks like, and where ML adds value vs. simpler approaches
• Design and maintain feature engineering pipelines for model development
• Monitor model quality in production, tracking performance over time, detecting data drift, and determining when to retrain
• Partner closely with leadership, go-to-market, fraud operations, product, and engineering teams to define and execute effective fraud strategies
• Champion a culture of continuous learning, experimentation, and collaboration across the fraud and broader data science teams

Senior Machine Learning Data Scientist

Product Manager

• You run intake from across the company, prioritize against business value and technical readiness, and keep the queue of business-system connectors moving.
• You lead exec-prioritized cross-functional projects from intake through ship.
• You triage requests, manage office hours, and run the AI Builds and champion support channels.
• You hold the map of who owns what across DevX, DevOps, Product Engineering, and the network of Champions in the business.
• You run the planning, check-in, and reflect cadences.

Full-stack Engineer

• Design and ship secure MCP (Model Context Protocol) connectors to Extend's internal systems and the third-party SaaS we run on: finance, CRM, data warehouse, expense management, product analytics, support, ATS, and the long tail beyond.
• Build and curate the shared library of agent skills that every team at Extend composes from. Ship skills, codify patterns, and raise the floor for what a safe, high-quality skill looks like.
• Extend our agent infrastructure. Build the tooling that lets non-engineers create reusable agent skills securely and reliably. Encode the review and publishing model for shared tooling, shared runtimes, and the feedback loop on agent behavior in production. Fill the open phases of the lifecycle that governs how skills are designed, reviewed, and shipped, so non-engineers can build and ship intelligent automation end to end.
• Build toward a connector-building agent: a meta-agent that discovers APIs, scaffolds MCP servers, and provisions access automatically. The end state is a platform that is itself an agent.
• Work with our platform teams to establish the credential scoping, OpenTelemetry instrumentation, and least-privilege patterns that every connector and skill ships with, so security is built in from day one.
• Own the employee experience for the agentic platform. Help onboard employees to the tools with self-serve guides, build skills people can learn from, and run the feedback loop between what's shipped and what adopters actually need. Your job isn't done when the connector ships. It's done when the team using it is self-sufficient.
• Design credential scoping and vending for agent connectors: how API keys are provisioned, rotated, and scoped per user, per skill, per connector. OAuth/OIDC where it fits, least-privilege everywhere.
• Build the risk-tier and review model for shared agent skills: what's safe at personal, team, and org level; sandboxing strategy; malicious dependency scanning for skills that pull in untrusted packages.
• Instrument the agent platform end-to-end with OpenTelemetry: every MCP call, every skill execution, every credential use is visible in Coralogix.

Senior AI Software Engineer, Internal Enablement

Data Engineer

• You own our data warehouse and the reporting layer on top of it, setting patterns for how data is modeled, evolved, and exposed.
• You write SQL and dbt models, refactor transformations, and build the tables and views downstream teams rely on.
• You proactively engage with teams across the company to understand how data is created and used, identify gaps, and guide solutions.
• You partner with our DevX and architecture teams on the boundary between product engineering services and Snowflake.
• You build models, tests, and processes that anticipate malformed data and upstream changes, making our pipelines boring to operate.
• You instrument what you own, define meaningful SLOs and data quality checks, and participate in our rotating on-call schedule.
• You own and extend our Python jobs running on Glue, Lambda, and Step Functions.
• You pair with more junior engineers on real work, raise the bar on PR and architecture reviews, and define the patterns and standards the team writes against.

Principal Data Engineer

Machine Learning Engineer

• Own the model lifecycle: requirements, experimentation, model development, evaluation, and model cards, partnering with ML engineers on deployment and production infrastructure
• Translate business problems into well-framed ML solutions: defining what to model, what success looks like, and where ML adds value vs. simpler approaches
• Design and maintain feature engineering pipelines for model development
• Drive experiment design and statistical rigor: ensuring models are evaluated with sound methodology before and after launch
• Monitor model quality in production, tracking performance over time, detecting data drift, and determining when to retrain
• Cultivate a culture of learning and collaboration within and across partner teams
• Perform design and code reviews to raise the technical excellence bar
• Hire, mentor, and coach data scientists

Manager, Machine Learning

Operations

• Real-Time Claim Review: Review claims in real time for potential cases of fraud/abuse, leveraging your information gathering and communication skills to ensure you have sufficient evidence to make a decision, and providing valuable feedback for Extend’s risk models.
• Risk Score Judgments: Review customer behavior and incidents using internal and external data and independently make decisions on customer risk. During the ramp-up period, you will only make judgments on risk scores; however, afterwards, you will also have the power to recommend denials and approvals for claims.
• Fraud Investigations and Subject Matter Expertise: Bring your own experience to perform fraud investigations and deliver insights on emerging trends for fraud/abuse.
• Follow Procedure: Take ownership of each investigation and diligently follow all regulatory and departmental practices and procedures, making independent decisions based on facts and evidence, and reporting any non-standard escalations to your manager. Attend regular meetings and training for Fraud Operations best practices.
• Cross-functional Work: Collaborate with Claims, Product, and Engineering, completing ad hoc tasks to evaluate and improve in-house models to detect fraud.
• Be Proactive and Adaptable: Drive team success and achieve both independent and collaborative goals, proactively completing additional tasks for the role as needed.

Fraud Operations Specialist I

Analyst

• Build and maintain models to support business cases, operational planning, and strategic decisions
• Develop and own merchant-facing reporting models and data visualizations that communicate performance clearly and compellingly
• Partner with sales, merchant success and growth teams to deliver data and analysis that helps retain and upsell merchants
• Respond to adhoc data requests from internal stakeholders including GSMs and AEs, providing timely and accurate insights
• Work closely with data engineering and product teams to stay aligned on underlying data changes and surface opportunities for improvement
• Contribute to building a more scalable, sophisticated reporting pipeline over time

Client Insights Analyst

• Cross-functional Champion: Independently lead cross-functional teams through ambiguous questions to influence and implement revenue driving initiatives and recommendations.
• Revenue Optimization: Own and identify innovative new strategies to maximize revenue and enhance client acquisition throughout the entire revenue waterfall.
• Data-driven Insights: Lead the charge in improving our quantitative analysis techniques (simulation models, A/B testing, causal impact analysis, etc.) to better influence audiences on strategic direction, with opportunities to present these insights to leadership on a regular basis.
• Data Infrastructure: Own, refine, and improve existing data pipelines to harness the power of data analytics, defining data structures, and leveraging advanced tracking methodologies to uncover revenue opportunities.
• Role Model: Set the standard for the team in terms of analytical thinking and excellence.

Senior Data Scientist, Revenue Analytics

• Own the monthly revenue forecast process and assist in the quarterly/annual financial planning of Extend.
• Build reports and dashboard to help monitor / track revenue performance in a timely manner and make business recommendations based on the monitoring.
• Perform exploratory data analysis to assemble metrics and KPIs to assess and gauge activity across the Extend platform, and support teams throughout Extend by delivering analyses and process improvements to help the business continue to scale.
• Support business operations, finance, revenue operations, and other internal teams by building analytics and reporting to understand overall business performance and increase organizational efficiencies.
• Work with data engineering and product teams to ensure proper data tracking is set up for features and products, and support debugging and development of new ETL processes.
• Collaborate with stakeholders across the organizations to establish the highest business priorities and opportunities by measuring expected reach and impact for operations focused teams.
• Work with our Risk & Insurance team to build deep-dive analytics to help drive step-change improvements in efficiency (beyond day-to-day workforce management).

Senior Data Scientist, Finance Analytics

• Identify high-impact opportunities to improve claim operations, servicing and fulfillment processes by analyzing trends, surfacing gaps, and delivering recommendations to enhance user experience while managing operational costs effectively.
• Monitor usage trends and anomalies in claim process, measure impact of new product features, and evaluate and iterate AI driven solutions to optimize agent behaviors and ensure stability of agent interactions
• Cultivate a data-driven culture with your skill for storytelling with data, translate analyses into clear, compelling narratives and visualizations that enable informed decision-making across stakeholders.
• Partner widely with Analytics, Operations, Product and Engineering to optimize claim intake, adjudication and fulfillment product features for excellent customer experience and lower claim cost.
• Ensure data quality and usability through regular monitoring and data pipeline creation, while empowering the teams with tools and documentation to optimize claim and servicing products and policies.