Senior Machine Learning Scientist

About the Role

We are looking for a Machine Learning Scientist with a passion for building software solutions where customer experiences take centre stage, products built with service quality at heart.

At Uber we are building a real-time data platform to enable customer experience observability and analytics at scale, key ingredients to ensure we deliver best-in-class experiences for our users - facilitating service reliability as well as informing product improvements, enabling both reactive and proactive service quality processes.

This is an outstanding opportunity for an applied ML scientist possessing a collaborative spirit to the core; who will work with engineering, product, design, and peer data scientists to help drive our ambitious customer experience observability platform. It’s a high-impact role where you will collaborate on challenges across a wide range of fields, working with teams across domains, aligning with stakeholders across functions.

You enjoy building solutions that take into account both the customer experience on one side (spenders and earners as the subject of study) and the tooling experience on the other (developers, product managers and data scientists as users).

If you have the technical chops, we invite you to come join our team to solve tough large-scale data challenges, develop data insights and raise the bar of service quality at Uber.

• Help design and build the next generation customer experience observability platform for all Uber apps, capable of detecting and alerting on degradations in customer experience based on a real-time 360° view of behavioural tracking data, with analytics events being sourced across domains and across the stack, covering user interactions as well as underlying business processes across all business verticals on Uber’s commerce platform globally.
  
  The platform enables business performance measurement via a unified set of derived business metrics, powering service health monitoring as well as business analytics - informing product improvements with deeper business insights. Techniques being applied today include multivariate time-series anomaly detection, correlation of key events such as code and config changes, as well as real-time XP feature rollout monitoring. Maturing these techniques at Uber scale is a major challenge.
• Collaborate across teams and across functions on jointly building a live machine-readable knowledge base of customer experiences and respective user flows serving as the basis for smart AI based alert triage, automated incident mitigation and root cause analysis.
• Build tooling for assisted onboarding of flows and respective metrics by suggesting funnels and sub-flows based on process mining techniques, recommend anomaly detection hyper-parameters based on past incidents and alert annotations, etc.
• Help oversee the delivery of business observability solutions across teams, advising on strategic engineering investments and the tactical prioritisation of projects to gain adequate observability, enabling monitoring and insights driving the biggest impact in improving customer experiences based on the gaps and needs of each domain.