Senior Applied Scientist Job at Uber (Amsterdam)

Senior Applied Scientist

Uber

Location:
Netherlands , Amsterdam

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Not provided

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Job Description:

We are looking for a Senior Applied Scientist with a passion for building software solutions where customer experiences take centre stage and products are built with service quality at heart. 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. The platform helps detect and respond to degradations in customer experience, supports safe code deployments and fast feature rollouts through real-time monitoring, and powers deeper analytics that inform product improvements, enabling both reactive and proactive service quality processes.

Job Responsibility:

Design and improve state-of-the-art anomaly detection and alerting for multivariate time series metrics
Build methods to reduce incident impact, such as by shortening incident time-to-detection and time-to-resolution while reducing alert fatigue
Contribute to intelligent incident response workflows: auto-triage to right team, suspected root-cause hints, auto-mitigation actions as well as agentic mitigation flows
Develop statistical monitoring approaches for code deployment safety and feature rollout safety
Support safe and fast product releases by adjusting code deployment soak times or feature rollout speed based on statistical significance in guardrail metrics
Partner with Engineering on building data infrastructure producing 'analytics-ready' datasets
Define best practices in instrumentation and metric definitions to facilitate incident detection
Contribute to monitoring converge assisted observability and monitoring
Define success metrics for incident detection systems and create evaluation harnesses using historical incidents and annotated alerts
Communicate results clearly to technical and non-technical stakeholders
drive alignment on tradeoffs, OKRs and roadmap

Requirements:

M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, Operations Research, Economics, or another quantitative field
6+ years of proven experience as an Applied Scientist, Machine Learning Scientist/Engineer, Research Scientist, or equivalent
Strong expertise in causal inference / experimentation, including designing, executing, and analyzing A/B tests
Strong expertise in anomaly detection and time-series analysis, with hands-on experience building production-grade, scalable detection and alerting pipelines for large-scale, real-time systems
Experience in production coding and deployment of ML, statistical, causal, and/or optimization models in real-time or near-real-time systems
Ability to use Python (or similar languages) to work efficiently at scale with large datasets in production environments
strong software engineering fundamentals
Proficiency in SQL and distributed data processing (e.g. PySpark, Flink SQL)
Excellent communication skills in cross-functional settings, with demonstrated ability to translate business/system problems into technical solutions and influence stakeholders
Thought leadership and ownership to drive multi-functional initiatives from conceptualization through productionization

Nice to have:

Experience with real-time or near-real-time pipelines and large-scale data systems (e.g., Spark, streaming, Kafka-like systems, OLAP stores)
Experience in observability, user analytics, experimentation platforms, or reliability monitoring
Familiarity with event correlation and change attribution (e.g., linking regressions to code/config/feature flag changes)
Experience building tools that improve workflow quality (onboarding, annotation, diagnosis dashboards)

Additional Information:

Job Posted:
March 21, 2026

Employment Type:

Fulltime

Work Type:

Hybrid work

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