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Senior Data Scientist
Takeaway.com. Lead the design of agent systems, including agent and workflow architecture, tool integration and human handover .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and operating user-facing AI agents, with a strong focus on agent evaluation strategies, observability, and production monitoring. Proficient in Python and SQL, with a solid foundation in data science and software engineering practices.
Highest-signal resume keywords
User-Facing AI Agent DesignAgent Evaluation StrategiesPython ProgrammingCI/CD PracticesAgent Observability and Monitoring
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonSQLAgent FrameworksAgent Evaluation MetricsRegression Testing AutomationFeature ManagementExperiment DesignCausal InferenceMaintainable System DesignData Privacy and Security
Soft Skills
Strong Communication SkillsMentoring Junior ColleaguesInfluencing Without Formal Authority
Tools & Technologies
OpenTelemetryDatadogGoogle ADKLangGraphDockerKubernetes
Industry Keywords
Agent SystemsProduction MonitoringQuality MonitoringMulti-Agent ArchitecturesStaged Rollouts
Tech Stack
Tools & technologiesDockerKotlinKubernetesPythonSQL
About the role
Key responsibilities & impact- Lead the design of agent systems, including agent and workflow architecture, tool integration and human handover
- Evaluate new models, frameworks and agent patterns and recommend when to adopt them
- Define end-to-end agent evaluation using offline datasets, LLM-as-a-judge, deterministic metrics, automated testing and online evaluation
- Establish standards and shared tooling for reliable ship/no-ship decisions
- Own production monitoring of tracing, quality, cost and latency signals and alerting
- Investigate regressions and incidents and turn production traces and user feedback into prioritised improvements
- Define release and rollback processes using feature management, staged rollouts and guardrails
- Drive testing, code review and CI/CD practices for safe delivery
- Ensure data privacy, security and compliance are built in
- Translate product requirements into technical plans, break down complex work, flag risks and set timelines
- Identify and prioritise technical debt and platform investments
- Quantify business impact through experimentation and causal reasoning
- Communicate trade-offs and uncertainty to senior stakeholders
- Guide Data Scientists through code and design reviews, pairing and feedback
- Help shape hiring, onboarding and team practices
- Collaborate with Backend, Operations and Product teams
- Grow an internal community sharing patterns, lessons learned and reusable tooling
Requirements
What you’ll need- Substantial experience designing, building and operating user-facing AI agents in production
- Deep experience designing agent evaluation strategies and datasets
- Experience building and calibrating LLM-as-a-judge metrics
- Experience automating regression testing and connecting offline results to online outcomes through experiments
- Deep experience with agent observability and monitoring, including tracing, quality monitoring, metrics and alerts
- Experience with OpenTelemetry, Datadog, or similar tooling
- Strong hands-on experience with agent frameworks such as Google ADK or LangGraph
- Experience with tool use / MCP and multi-step or multi-agent architectures
- Experience defining or operating release processes for production ML or agent systems, including feature management, staged rollouts and rollback
- Excellent Python and SQL
- Strong software engineering practices including typing, testing, CI/CD, code review and maintainable system design
- Strong data science foundation in statistics, experiment design and causal inference
- Track record of leading technical work across teams and taking projects from idea to production
- Experience mentoring more junior colleagues
- Strong communication skills and ability to influence without formal authority
- Nice to have: experience designing agent evaluation strategy at organisational scale
- Nice to have: containerised deployments using Docker/Kubernetes
- Nice to have: basic understanding of Kotlin
Benefits
Comp & perks- Hybrid working arrangement: 3 days a week from the office and 2 days working from home
- Inclusive culture supporting diversity and belonging
- Supportive, fast-paced work environment
- Growth opportunities
- Employee celebrations and recognition of wins