New opportunity

Lead AI Analyst

Impact Analytics · Bangalore, India

About this role

Role Overview: As a Lead AI Analyst at Impact Analytics, you will operate at the intersection of data analytics, statistics, and AI-driven product insights. You will be responsible for leading end-to-end analytical projects, defining business metrics, and translating complex datasets into actionable recommendations to drive product and business outcomes in the retail merchandising space. Responsibilities: Lead analytical projects from problem definition to solution development and validation. Develop solutions using Python and SQL to process and interpret large datasets. Apply statistical methods, including hypothesis testing and regression analysis, to solve business problems. Define and track KPIs and perform product analytics such as funnel, cohort, and customer segmentation analysis. Design and maintain analytical data models. Collaborate with Product, Engineering, and Data Science teams to build scalable solutions. Explore and implement AI and Generative AI solutions to enhance analytical workflows. Mentor team members and establish best practices for data analysis. Required Qualifications: 5-8 years of experience in product analytics, business analytics, or data science. Strong proficiency in Python (Pandas, NumPy) and SQL (complex joins, window functions, query optimization). Solid foundation in statistics, data modeling, and data warehousing concepts. Experience with large datasets and visualization tools like Tableau or Power BI. Strong logical reasoning and communication skills. Preferred Qualifications: Experience in AI/ML-driven products, SaaS, retail analytics, e-commerce, or demand forecasting. Familiarity with cloud data platforms like Snowflake, BigQuery, or Redshift. Exposure to Generative AI applications.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonSQLPandasNumPyExploratory Data AnalysisStatisticsHypothesis TestingRegression AnalysisData ModelingData WarehousingProduct AnalyticsTableauPower BIGenerative AIMachine Learning

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

End-to-End Analytical Project Leadership
The role requires managing the full lifecycle of analytical projects, from initial problem definition and data exploration to final validation and stakeholder presentation. You must demonstrate the ability to translate ambiguous business needs into structured, scalable analytical solutions while maintaining high standards for code quality and methodology.
Retail-Centric Metric Definition and Tracking
You will be responsible for defining and monitoring KPIs specific to retail merchandising, such as demand forecasting, pricing, and promotion effectiveness. Success depends on your ability to perform deep-dive product analytics, including cohort and funnel analysis, to provide actionable insights that directly influence business outcomes.
Integration of Generative AI into Workflows
Beyond traditional data science, you are expected to actively explore and implement Generative AI applications to enhance analytical productivity. You must be prepared to bridge the gap between standard statistical modeling and modern AI-driven product features.

Likely challenges to think through

Balancing Technical Rigor with Business Urgency
As a lead in a fast-paced, series D retail SaaS environment, you will face the challenge of delivering high-quality, statistically sound models under tight product development timelines. Prepare by showcasing examples of how you have optimized SQL queries or automated data pipelines without sacrificing the accuracy of your statistical findings.
Cross-Functional Communication of Complex Insights
You will frequently translate complex technical findings for non-technical stakeholders in Product and Engineering. The difficulty lies in maintaining the nuance of your statistical analysis while ensuring the recommendations are clear and actionable for business decision-makers. Practice framing your past projects by focusing on the 'so what' for the business rather than just the technical methodology.

Practice your answers

Technical practice

Optimizing SQL Queries for Large Datasets

What to coverExplain your approach to optimizing slow-running SQL queries involving complex joins and window functions. Discuss how you evaluate execution plans, the role of indexing, and when to use CTEs versus temporary tables to improve performance in a data warehousing environment.

Statistical Rigor in Product Analytics

What to coverDescribe how you would design a hypothesis test to evaluate the impact of a new retail merchandising feature. Address how you determine sample size, select appropriate metrics, and account for potential biases or confounding variables in your analysis.

Data Modeling for Retail Merchandising

What to coverHow do you approach designing a star schema or snowflake schema for a retail analytics platform? Focus on how you handle data granularity, fact and dimension tables, and ensure the model remains scalable for demand forecasting use cases.

Python for Exploratory Data Analysis

What to coverWhen faced with an ambiguous, large-scale dataset, what is your workflow for performing exploratory data analysis using Pandas and NumPy? Emphasize how you identify anomalies, handle missing values, and validate data quality before proceeding to modeling.

Integrating Generative AI into Analytical Workflows

What to coverSuggest ways to leverage Generative AI or LLMs to enhance productivity in data analysis tasks. Discuss the trade-offs between using AI-generated code versus manual implementation, particularly regarding accuracy, security, and maintainability.

Regression Analysis for Business Outcomes

What to coverProvide an example of how you have used regression analysis to solve a business problem, such as pricing or demand forecasting. Explain how you validate the model's assumptions and how you translate the coefficients into actionable business recommendations.

Product Metrics and Funnel Analysis

What to coverHow do you define and track KPIs for a SaaS product? Explain your process for conducting cohort analysis to understand user retention and feature adoption, and how you communicate these findings to non-technical stakeholders.

Data Visualization Best Practices

What to coverWhen building dashboards in Tableau or Power BI, how do you ensure the visualization effectively communicates complex trends to business leaders? Discuss the importance of context, interactivity, and choosing the right chart types for different data distributions.

Behavioural practice

Leading Analytical Projects

What to coverDescribe a time you led an end-to-end analytical project from problem definition to final recommendation. Focus on how you managed stakeholder expectations, handled ambiguity, and ensured the final solution was adopted by the business.

Mentoring and Quality Assurance

What to coverProvide an example of how you have mentored team members to improve their analytical skills or code quality. Explain the specific feedback you provided and the resulting impact on the team's output or best practices.

Cross-Functional Collaboration

What to coverTell me about a situation where you had to collaborate with Engineering or Product teams to implement a data-driven solution. How did you resolve technical disagreements or align on the definition of success metrics?

Handling Ambiguity in Fast-Paced Environments

What to coverDescribe a scenario where you were given a vague business problem and had to define the analytical approach yourself. How did you break down the problem, prioritize your tasks, and ensure your findings were relevant to the business goals?

Sharpen your knowledge

Topics to prepare

Retail Merchandising Analytics
Focus on understanding retail-specific metrics such as pricing, promotions, inventory turnover, and demand forecasting. Exercise: Research how retail KPIs like sell-through rate and gross margin are calculated and how they influence merchandising decisions. This is relevant as the role centers on transforming retail merchandising through AI.
Product Analytics Frameworks
Review methodologies for funnel analysis, cohort analysis, and customer segmentation. Exercise: Design a hypothetical cohort analysis to measure feature adoption for a new retail software tool. This is essential for the requirement to define and track product KPIs and measure business outcomes.
Statistical Inference in Business
Deepen knowledge of hypothesis testing, confidence intervals, and regression analysis. Exercise: Formulate a business problem (e.g., impact of a promotion on sales) and determine the appropriate statistical test to validate the result. This aligns with the requirement to apply statistical methods to solve business problems.

Skills to revise

Advanced SQL Querying
Practice writing complex joins, window functions, and CTEs for large datasets. Exercise: Optimize a multi-join query that aggregates sales data across different product categories using window functions. This is a core requirement for processing and interpreting large datasets in this role.
Python Data Manipulation
Master Pandas and NumPy for cleaning and transforming complex datasets. Exercise: Create a script to clean a messy retail dataset, handle missing values, and perform exploratory data analysis to identify anomalies. This is directly required for the Lead AI Analyst's daily analytical workflows.
Data Modeling
Understand star schemas, fact/dimension tables, and data warehousing principles. Exercise: Sketch a data model for a retail merchandising system that connects sales transactions to product and store dimensions. This is necessary for designing and maintaining the analytical data models mentioned in the job description.

Your preparation checklist

Prepare a retail-focused analytical case study
Develop a portfolio project that demonstrates your ability to solve a retail merchandising problem, such as demand forecasting or customer segmentation. Include a clear problem definition, the statistical methods used (e.g., regression analysis), and the business impact of your findings.
Demonstrate advanced SQL and Python proficiency
Create a repository of code snippets showcasing your ability to handle large datasets using Pandas and NumPy, and complex SQL queries involving window functions, CTEs, and query optimization techniques relevant to data warehousing.
Map your experience to product analytics frameworks
Prepare to discuss how you have previously defined KPIs and conducted funnel or cohort analysis. Be ready to explain how you translate these metrics into actionable product recommendations for non-technical stakeholders.
Research Generative AI applications in analytics
Identify and document specific ways you could use Generative AI to improve analytical workflows, such as automating data cleaning, generating SQL queries, or summarizing complex analytical reports to increase team productivity.
Formulate questions about the retail domain and team structure
Prepare thoughtful questions for the interviewers regarding the specific retail merchandising challenges Impact Analytics is currently solving, how the Data Science team collaborates with Engineering, and the current data stack (e.g., Snowflake or BigQuery) used for their AI products.

Availability has not yet been verified. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in India. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…