Swiggy is hiring for a FRESHER entry level Associate Data Scientist role in India

Position:

• Associate Data Scientist

• Entry level role designed for early career professionals starting their journey in applied data science

• Focused on supporting live data science systems in a real world production environment

• Acts as an operational backbone for analytical and machine learning workflows

• Serves as an initial ownership role for monitoring and stability of deployed models

• Opportunity to grow into advanced analytical or machine learning focused roles over time

Company:

• Swiggy

• One of India’s largest consumer technology companies

• Operates at the intersection of logistics, analytics, and large scale digital platforms

• Known for data driven decision making across business functions

• Strong culture of experimentation, automation, and engineering excellence

• Works with massive volumes of real time data across multiple consumer services

Location:

• Bangalore

• Embassy Tech Village

• Office based role with on site work expectations

• Located within one of India’s largest technology hubs

• Offers exposure to cross functional teams working from the same campus

Job type:

• Full time

• Long term employment opportunity

• Role aligned with core data science operations

• Includes on call responsibilities as part of production support

• Designed for stability and growth rather than short term contract work

Job mode:

• Onsite

• Work from office model

• Daily collaboration with data science, engineering, and platform teams

• Direct access to monitoring dashboards, tools, and operational infrastructure

Job requisition id:

• 23013

• Used internally for hiring and application tracking

• Helps align the role with specific teams and domains

• Referenced during internal escalation and documentation

Years of experience:

• Zero to one year

• Suitable for fresh graduates or professionals with limited industry exposure

• Ideal for candidates transitioning from academic learning to production systems

• Encourages learning through hands on operational exposure

• No requirement for extensive prior industry experience

---

Company description

• Swiggy is an Indian consumer internet company built around convenience and speed

• The platform serves millions of users across multiple urban and semi urban locations

• Its ecosystem includes food delivery, quick commerce, dining services, and logistics

• Swiggy Food connects customers with a vast network of restaurants across hundreds of cities

• Swiggy Instamart focuses on ultra fast delivery of groceries and daily essentials

• The company continuously expands its service portfolio through innovation and experimentation

• Technology is at the heart of every product and service offered by Swiggy

• Data science plays a central role in personalization, pricing, logistics optimization, and growth

• Large scale real time data pipelines support consumer experiences and operational decisions

• Teams work with complex datasets that reflect customer behavior, supply constraints, and delivery dynamics

• Swiggy invests heavily in engineering platforms, observability, and analytics tooling

• Employees work in cross functional teams involving data scientists, engineers, and product managers

• The organization promotes a culture of ownership, learning, and accountability

• Rapid growth has created opportunities for early career professionals to take responsibility early

• Swiggy values diversity, inclusion, and fairness across all hiring and employment practices

• The company follows equal opportunity employment principles in all regions

• Continuous improvement and post incident learning are core organizational values

• Employees are encouraged to document learnings and improve systems proactively

• Swiggy’s scale offers exposure to real world challenges not commonly found in smaller firms

• Working at Swiggy provides experience with high traffic platforms and production grade systems

---

Profile overview

• The Associate Data Scientist role is focused on ensuring stability of data science systems in production

• The position acts as the first response when automated alerts or incidents are triggered

• The role sits at the intersection of analytics, engineering, and operational support

• Individuals in this role monitor systems that power decision making and customer experiences

• The work involves responding to alerts generated by metrics, pipelines, or deployed models

• A structured approach is followed to investigate and resolve issues efficiently

• The role requires understanding both technical and analytical aspects of systems

• Candidates learn how models behave after deployment in real world environments

• Exposure includes data pipelines, feature stores, dashboards, and monitoring frameworks

• The associate identifies whether an issue is due to data quality, infrastructure, or model behavior

• Clear escalation paths are followed when deeper intervention is required

• Incident ownership includes documenting root causes and resolutions

• The role emphasizes discipline in communication and handover between teams

• Associates work closely with senior data scientists and engineers

• Over time, individuals gain insight into model experimentation and deployment cycles

• The position provides a strong foundation for transitioning into core data science roles

• Learning occurs through real incidents rather than purely theoretical exercises

• The environment encourages curiosity and systematic troubleshooting

• Associates are trained to distinguish expected fluctuations from genuine anomalies

• The role builds confidence in handling time sensitive technical situations

• Long term growth paths include data scientist and machine learning engineering roles

---

Qualifications

• Basic understanding of data science concepts and workflows

• Hands on familiarity with Python for data handling and debugging

• Working knowledge of SQL for querying and validating datasets

• Exposure to distributed data processing concepts using Spark is beneficial

• Understanding of monitoring and observability concepts in production systems

• Familiarity with tools such as Grafana or similar dashboard platforms

• Ability to read logs and interpret system behavior

• Basic statistical intuition to evaluate key performance indicators

• Awareness of noise versus meaningful deviations in metrics

• Strong analytical thinking and structured problem solving skills

• Ability to remain calm and methodical during incident situations

• Clear written and verbal communication skills

• Willingness to document findings and update playbooks

• Openness to working in on call rotations as part of team responsibility

• Interest in learning how models behave after deployment

• Comfort working with cross functional stakeholders

• Understanding of incident response principles is an advantage

• Ability to follow predefined runbooks accurately

• Attention to detail when validating data and system health

• Commitment to continuous learning and improvement

---

Additional info

• Each domain follows a well defined Incident Response Plan

• Alerts are triggered based on metrics such as delays or sharp performance drops

• Response steps include checking dashboards, logs, and system health indicators

• Domain specific tools support deeper investigation during incidents

• Escalation thresholds are clearly defined to avoid unnecessary delays

• Associates are responsible for initiating escalation when required

• Post incident documentation is a key responsibility

• Root cause summaries help prevent recurrence of similar issues

• Resolution notes support learning across teams

• The role offers strong exposure to real time operational challenges

• On call responsibilities are balanced with learning and mentorship

• Performance is evaluated based on reliability and quality of incident handling

• The company encourages learning through post incident reviews

• Knowledge sharing is promoted through updated documentation

• Associates gain familiarity with production grade systems early in their careers

• The work environment supports growth through feedback and guidance

• Ethical hiring practices ensure fairness and inclusion

• Candidates from diverse academic and professional backgrounds are encouraged

• The role suits individuals who enjoy hands on problem solving

• Exposure gained here supports long term careers in data and machine learning

Please click here to apply.