Senior Product Analyst

Location - Bangalore (Hybrid)

Notice Period - Immediate Joiners (upto 15days)

About us

Zemoso is an AI-native product engineering company helping Fortune 500 companies and

high-growth enterprises turn complex ideas into production.

We work with enterprise teams to build production-grade AI agents for real business workflows,

driving measurable outcomes and ROI. We also help engineering organizations modernize how

software is built, applying AI across the SDLC to move from idea to production faster.

Zemoso has contributed to over $10 billion in value creation across healthcare, financial

services, logistics, biotech, retail, and e-commerce.

We are a 3x Deloitte Fast 50 company and an O'Reilly Architectural Katas winner. Our

technology partnerships include Anthropic, Databricks, Google Cloud and AWS.

Role overview

We are hiring a Senior Product Analyst with 5+ years of experience in product analytics,

experimentation and causal inference to join our team in Bangalore. You will turn ambiguous

product questions into rigorous analytical frameworks, partner with Product, Design, and

Engineering to define KPIs, and influence product roadmaps with data-driven insights.

What You will do

● Play the role of Senior Product Analyst, acting as the primary analytics thought partner to

Product, Engineering, and Science teams within cross-functional pod structures.

● Scope ambiguous product questions with stakeholders, define North Star metrics,

establish success and guardrail metrics, and deliver unbiased findings that directly inform

roadmap decisions.

● Design and execute A/B experiments end-to-end: define target audiences, determine

traffic sizing, execute power analysis, implement variance reduction (CUPED), manage

multiple-comparison corrections, and monitor metrics post-launch.

● Guide experiment tracking setup by defining event action requirements, dropping proper

experiment tags (XT tags), and validating telemetry pipelines before data analysis begins.

● Apply causal inference techniques and Quasi-Experimental Alternatives to measure

feature impact where randomized controlled trials (RCTs) are unfeasible or unethical.

● Perform deep-dive product analytics—including multi-step funnel optimization, cohort

tracking, and retention analysis—to pinpoint drop-offs and drive feature prioritization.

● Lead strategic analysis beyond standard experiment readouts, including quarterly strategy

planning, opportunity sizing, and metric deep dives to uncover customer needs.

● Build dashboards and consolidate findings into executive-ready experiment readouts to

drive go/no-go launch decisions in leadership reviews.

● Work with data engineers to design, build, test, and monitor scalable, reusable data

pipelines for ongoing experimentation and analytics workflows.

Basic Qualifications

● Experience: 5+ years.

● Hands-on experience as a Product Analyst / Data Analyst in product analytics, preferably

in cross border e-commerce, two-sided marketplaces, search domains or logistics.

● Strong experience designing and analyzing A/B experiments: power analysis, hypothesis

and significance testing, CUPED, Bonferroni correction.

● Deep expertise in causal inference methods and Quasi-Experimental Alternatives,

specifically:

○ Regression Discontinuity (RDD): Exploits administrative cutoffs or scoring rules

to mimic local randomization.

○ Propensity Score Matching: Statistically balances confounding covariates

between self-selected groups.

○ Difference-in-Differences (DiD): Compares treatment and control trends over

time, adjusting for baseline offsets.

○ Synthetic Controls: Constructs a weighted combination of unexposed units to

model a perfect counterfactual.

● Advanced SQL and coding experience in Python (PySpark a plus) on large data sets.

● Experience with tracking architecture, validating event telemetry, and defining tracking

requirements for engineering teams.

● Experience with funnel analysis, cohort analysis, retention analysis, KPI design, North

Star metrics, and opportunity sizing.

● Experience with data warehouse and pipeline tools such as Snowflake, Databricks and

Apache Airflow.

● Experience building dashboards in BI tools such as Power BI, Looker Studio, Sigma or

Metabase, and with product analytics tools such as Mixpanel or Amplitude.

● Strong communication and conflict management skills, with a track record of driving

transparency via tools like Airtable and presenting unbiased insights to cross-functional

leadership.

● Bachelor's or Master's degree in Mathematics, Statistics, Applied Mathematics or a

related quantitative field.

Nice to have Qualifications

● Experience with search analytics, including autocomplete optimization, ranking/relevance

model evaluations, or agentic/conversational search experiences.

● Familiarity with recommendation systems: collaborative filtering, content-based and hybrid

approaches, applied to personalization use cases.

● Exposure to Retrieval Augmented Generation (RAG) and LLM application development in

Python, including vector databases and retrieval pipelines.

● Experience building automated reporting workflows that reduce manual analyst effort.

● Experience with Git/GitHub for version-controlled analytics code.