Associate Data Scientist
SwiggyIndia18 hours ago
Full-timeEngineering, Information Technology
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Job Title: Associate Data Scientist
Location: Bangalore (WFO)
Tenure: 0-1 Years
About The Team And Role
The Associate Data Scientist will play a critical role in ensuring production stability of data science systems. The engineer will be the first line of defense when alerts or incidents occur in production. The on-call support engineer role provides strong exposure to the intersection of Data Science, Engineering, and Operations. A career path can evolve into:
Location: Bangalore (WFO)
Tenure: 0-1 Years
About The Team And Role
The Associate Data Scientist will play a critical role in ensuring production stability of data science systems. The engineer will be the first line of defense when alerts or incidents occur in production. The on-call support engineer role provides strong exposure to the intersection of Data Science, Engineering, and Operations. A career path can evolve into:
- Data Scientist / ML Engineer: With upskilling, contributing to model design, experimentation, and deployment
- Monitoring automated alerts across domains.
- Executing defined incident response runbooks.
- Debugging issues using available RCA tools, observability tools, logs, and dashboards.
- Determining whether the issue is operational or model-related.
- Escalating to the model owner when necessary.
- Ensuring incident reports are logged with root-cause summaries and resolution notes.
- Every domain maintains a structured Incident Response Plan (IRP):
- Alerts – List of metrics and conditions that trigger an incident (e.g., data pipeline delays, sharp metric drops, API errors).
- Response – Step-by-step guide for initial checks (logs, dashboards, health checks).
- Tools to Debug – Domain-specific tools (Grafana, DBR notebooks, feature store dashboards).
- Escalation Triggers – Clear thresholds for escalation to model owners (data quality corruption, unexplained metric deviations).
- Monitoring & Debugging: Familiarity with observability tools (Grafana, Prometheus, Cloud Monitoring).
- Data Systems Knowledge: Hands-on with SQL, Python, Basic Spark
- Statistical Intuition: Ability to distinguish noise vs. genuine anomalies in KPIs.
- Problem-Solving: Strong troubleshooting skills under time pressure.
- Communication: Crisp incident communication and handoff to DS/engineering teams.
- Documentation Discipline: Updating incident logs, playbooks, and IRPs post-mortem.
Key Skills
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