Swiggy Hiring Associate Data Scientist | 0–1 Years Experience | Bangalore Work From Office
Swiggy is hiring for the role of Associate Data Scientist at its Bangalore location. This opportunity is designed for fresh graduates and early-career professionals with 0–1 years of experience who want to build a strong foundation in data science within a large-scale, real-world production environment. Swiggy’s data science teams play a critical role in ensuring platform reliability, monitoring complex systems, and enabling data-driven decision-making across engineering and operations.
This role is ideal for candidates who are interested in applied data science, system monitoring, incident management, and working at the intersection of data science, engineering, and operations. As an Associate Data Scientist, you will be part of the first line of defense for production data science systems and contribute directly to platform stability and business continuity.
Job Overview
- Company: Swiggy
- Position: Associate Data Scientist
- Experience: 0–1 Years
- Location: Bangalore, Karnataka, India
- Work Mode: Work From Office
- Employment Type: Full-Time
- Role Level: Entry Level
This role is suitable for candidates who want hands-on exposure to production data systems rather than purely academic or experimental data science work.
About the Team and Role
The Associate Data Scientist role at Swiggy focuses on maintaining the health, reliability, and performance of data science systems running in production. You will work closely with data scientists, engineers, and operations teams to monitor systems, respond to incidents, and ensure smooth functioning of data pipelines and models.
The role offers strong exposure to real-time system monitoring, incident response, and operational data workflows. Over time, this position can open up multiple career paths within Swiggy, including advanced data science, machine learning engineering, and data platform roles.
Key Responsibilities
- Monitor automated alerts across data science and machine learning systems
- Respond to production incidents using defined runbooks and escalation processes
- Debug issues related to data pipelines, dashboards, and reporting tools
- Identify whether incidents are operational or model-related
- Escalate issues to model owners or engineering teams when required
- Ensure incident reports are properly logged with clear root-cause documentation
- Maintain structured incident response playbooks and documentation
- Track alerts such as data pipeline delays, sharp metric changes, and API errors
- Follow step-by-step resolution guides for critical alerts
- Use team-specific tools such as Grafana, DBR notebooks, and feature store dashboards
- Monitor thresholds and manage escalations to ensure system stability
These responsibilities help ensure that Swiggy’s data-driven systems remain reliable and responsive at scale.
Skills Required
To succeed in this role, candidates should have the following skills:
- Strong debugging and troubleshooting abilities
- Familiarity with observability tools like Grafana or Prometheus
- Hands-on knowledge of SQL and Python
- Basic understanding of big data tools such as Spark
- Ability to distinguish between genuine anomalies and noise in data
- Strong problem-solving skills and logical thinking
- Clear communication skills for incident handling and handoffs
- Discipline in documentation, reporting, and post-incident analysis
Experience with data pipelines, monitoring systems, or production analytics is an added advantage but not mandatory for freshers.
What Makes This Role Unique
Unlike traditional entry-level data science roles that focus mainly on modeling or analysis, this position provides deep exposure to real production environments. You will learn how large-scale data systems behave under load, how incidents are detected and resolved, and how data science directly supports business-critical operations.
This experience builds a strong foundation for long-term careers in data science, machine learning engineering, and data platform reliability.
Career Growth Opportunities
Starting as an Associate Data Scientist at Swiggy can lead to multiple growth paths based on performance and interests, including:
- Data Scientist
- Machine Learning Engineer
- Data Platform Engineer
- Analytics Engineer
- Reliability or Observability Specialist
The hands-on exposure to production systems makes this role especially valuable for building practical, industry-relevant expertise.
Work Environment and Culture
Swiggy offers a fast-paced, collaborative work environment where teams operate with high ownership and accountability. The data science and engineering teams follow structured processes for incident management, continuous improvement, and knowledge sharing. Freshers receive guidance from experienced professionals while being encouraged to take initiative and learn independently.
The work-from-office setup enables close collaboration, faster learning, and better exposure to cross-functional teams.
Who Should Apply
This role is ideal for:
- Fresh graduates with a background in data science, computer science, or related fields
- Candidates with 0–1 years of experience in analytics, data engineering, or monitoring roles
- Individuals interested in applied data science and system reliability
- Professionals who enjoy troubleshooting and working with real-time systems
Candidates who value learning through real-world problem-solving will benefit most from this opportunity.
About Swiggy
Swiggy is one of India’s leading consumer technology companies, operating across food delivery, quick commerce, and logistics. The company relies heavily on data science and engineering to optimize delivery networks, improve customer experience, and scale operations across the country. Swiggy’s data teams play a vital role in powering intelligent decision-making and maintaining platform reliability.
How to Apply
Interested candidates can apply directly through Swiggy’s official careers portal. Ensure your resume highlights your technical skills, academic background, and any hands-on experience with data systems, monitoring, or analytics.
