About the Role
Work Arrangement: Employees are required to visit the Bangalore office once every quarter for one week.
Data Science at Swiggy
Data Science and applied machine learning (ML) play a critical role in shaping Swiggy’s decision-making and product development processes. Our data scientists collaborate closely with cross-functional teams to deliver end-to-end data-driven solutions, from defining business challenges in mathematical or ML terms to deploying them in production environments. We actively contribute to projects that directly impact customer experience and business performance. Additionally, we encourage open exchange of ideas and the publication of work, both internally and externally.
About the Ads Monetization Team
The Ads Monetization team focuses on developing and optimizing ML solutions for managing the entire ad lifecycle across Swiggy’s Food and Instamart business lines. This includes sourcing relevant ads, pricing strategies, and targeting using personalized, multi-objective-optimized user-response models. Given that ads operate in a high-throughput and low-latency environment at Swiggy, we prioritize scalable, pragmatic solutions.
Responsibilities
- Apply your expertise in ML, deep learning (DL), and statistics to develop cutting-edge solutions that enhance ad recommendations and improve campaign performance through various optimization techniques.
- Analyze Swiggy’s extensive historical data to generate insights and identify solutions to business and customer experience challenges.
- Collaborate with engineers, product managers, and analysts to design, develop, and implement end-to-end inference solutions that operate at Swiggy's scale.
- Stay updated on the latest advancements in ML research, particularly in Ads Bidding algorithms and Recommendation Systems, and adapt these innovations to Swiggy’s problem statements.
- Share your work with both technical and non-technical audiences in internal and external forums.
Qualifications
- Bachelor’s or Master’s degree in a quantitative field with 0-2 years of experience in the industry or research labs.
- Strong problem-solving skills with the ability to deconstruct and solve challenges from first principles.
- Hands-on experience in applying ML, DL, and statistical methods to real-world business problems.
- Preferred: Experience working with big data and deploying ML/DL models in production.
- Proficient in Python, SQL, Spark, TensorFlow.
- Strong verbal and written communication skills.
- A plus: Experience in the ecommerce or logistics domains.
