Quant Research Analyst

Zepto; Bengaluru; On-site; INR 1,200,000 - 1,500,000; Full time

Job summary

Zepto in Bengaluru is seeking a data science/quant research professional to build predictive models for pricing, promotions, customer behavior, and demand forecasting. You will apply ML and optimization techniques, work with Product, Engineering, and Supply Chain teams, and design A/B tests to validate your insights.

You will translate business problems into analytical models, communicate results to non-technical stakeholders, and contribute to scalable, production-ready solutions while staying

Qualifications

• 1–4 years of experience in a data science, quant research, or analytics role.
• Preferably in consumer tech, e-commerce, or finance.
• Degree in computer science, operations research, or a related field.
• Prior experience building and validating predictive models or optimization algorithms is preferred.
• Strong analytical and problem-solving skills; comfortable with ambiguity and complex systems.
• Proficiency in Python or R, and strong SQL skills for data extraction.
• Solid understanding of statistical inference, regression, time-series analysis, and/or machine learning techniques.
• Experience with data visualization tools like Jupyter, Plotly, matplotlib, or seaborn.

Responsibilities

• Conduct quantitative research to build models for pricing, promotions, customer behavior, and demand forecasting.
• Model logistics and assortment optimization using statistics, econometrics, and ML.
• Apply statistics, econometrics, ML, and optimization to generate actionable insights.
• Work cross-functionally with Product, Engineering, Category, Supply Chain, and Growth to translate business problems into models.
• Design and implement A/B tests and experiments to validate hypotheses and measure model performance.
• Present findings and strategic recommendations to stakeholders clearly and concisely.
• Collaborate with data engineering and product analytics to build scalable, production-ready solutions.
• Stay up-to-date with advancements in data science and contribute to knowledge sharing.

Skills

Python

R

SQL

Statistical modeling

Time-series

Machine learning

Optimization

Data visualization

Communication

Education

Computer Science / Operations Research / related field

Tools

Jupyter

Git

Plotly

matplotlib

seaborn

Job description

Responsibilities, aka, what we’d like you to deliver:

• Conduct quantitative research to build models related to pricing, promotions, customer

behavior, assortment, demand forecasting, and logistics.

• Apply techniques from statistics, econometrics, machine learning, and optimization to generate actionable insights.
• Work cross-functionally with Product, Engineering, Category, Supply Chain, and Growth teams to identify use cases and translate business problems into analytical models.
• Design and implement A/B tests and experiments to validate hypotheses and measure model performance.
• Present findings and strategic recommendations to stakeholders in a clear, concise, and
• Collaborate with data engineering and product analytics teams to build scalable,

production-ready solutions.

• Stay up-to-date with advancements in data science, ML, and quantitative methods, and actively contribute to knowledge sharing within the team.

Requirements:

• 1–4 years of experience in a data science, quant research, or analytics role, preferably in

consumer tech, e-commerce, or finance.

computer Science, Operations Research, or a related field.

• Prior experience building and validating predictive models or optimization algorithms is highly

preferred.

• Strong analytical skills and problem-solving ability; comfort with ambiguity and complex systems.
• Proficiency in Python or R, and strong SQL skills for data extraction and analysis.
• Solid understanding of statistical inference, regression, time-series analysis, and/or machine

learning techniques.

• Experience with tools like Jupyter, Git, and data visualization libraries (e.g., matplotlib, seaborn,

Plotly).

• Ability to communicate complex quantitative results to non-technical audiences effectively.
• Bonus: Experience with forecasting frameworks, optimization libraries (e.g., Gurobi, Pyomo), or