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Analytics Lead - UPI Product | Experience : 7-9 yrs

Paytm

  • Noida, India
  • Not disclosed
  • 7–9 Years
  • Posted Yesterday
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About the role

About Paytm

Paytm is India's leading mobile payments and financial services distribution company. Pioneer of the mobile QR payments revolution in India, Paytm builds technologies that help small businesses with payments and commerce. Paytm’s mission is to serve half a billion Indians and bring them to the mainstream economy with the help of technology.

About the Role

UPI is the backbone of India's digital payments revolution — and Paytm is one of its largest participants, processing hundreds of millions of transactions every month. We are looking for a Product Manager to join our UPI team and drive product strategy, user growth, and experience excellence across our UPI-powered flows.

This role sits at the intersection of consumer behaviour, financial infrastructure, and regulatory nuance. You will own high-impact product surfaces and work closely with engineering, design, data, and business teams to ship products that millions of Indians use daily.

Key Responsibilities

  • Analytics Strategy & Ownership
  • Own the end-to-end analytics charter for one or more UPI surfaces — from metric definition to instrumentation to insight generation
  • Build and maintain the source-of-truth framework for north-star and guardrail metrics across the UPI funnel (initiation, authentication, success/failure, retention)
  • Partner with NPCI-mandated reporting requirements and ensure analytical rigor aligns with regulatory data standards
  • Lead a team of analysts, setting priorities, reviewing outputs, and raising the bar on analytical craft
  • Funnel, Growth & Behavioural Insights
  • Diagnose friction points across the UPI journey using deep-dive analysis, cohort studies, and funnel decomposition
  • Build engagement, frequency, and top-of-wallet models to inform product and growth strategy
  • Design analytical frameworks for discovery and nudge effectiveness as new UPI capabilities are adopted
  • Partner with design and product research to combine quantitative and qualitative signals into a single narrative
  • Platform Reliability & Scale Analytics
  • Define and monitor SLA, success-rate, and latency benchmarks; build alerting and anomaly-detection frameworks tied to these metrics
  • Build root-cause analysis playbooks for payment failure spikes and incident-linked drop-offs
  • Model traffic and failure patterns ahead of high-volume events (festive seasons, IPL, government disbursements) to support platform readiness
  • Experimentation & Decision Science
  • Own the experimentation backbone — design, power, and evaluate A/B tests across the UPI funnel
  • Drive a test-and-learn culture: hypothesis → experiment → read-out → scale, with statistically sound methodology
  • Build self-serve dashboards and tooling that let product, engineering, and business teams access trusted insights without analyst dependency
  • Proactively surface insights that shape upstream roadmap and prioritization decisions

Required Qualifications

  • 7–9 years of analytics/data experience, with a mandatory focus on consumer-facing products or fintech; prior experience in payments or UPI strongly preferred
  • Strong command of SQL and hands-on experience with product analytics tools (Mixpanel, Amplitude, Metabase, or equivalent); working knowledge of Python/R for deeper statistical analysis
  • Solid grounding in experimentation design — hypothesis framing, sample size/power calculations, and interpreting A/B results correctly
  • Proven ability to translate ambiguous business questions into structured analyses and clear, decision-ready recommendations
  • Experience building and owning dashboards and metrics frameworks at scale, including data quality and governance practices
  • Strong written and verbal communication skills — ability to present findings crisply to leadership and align cross-functional stakeholders
  • Experience managing or mentoring a team of analysts, with a demonstrated ability to review work and raise analytical standards

Good to Have

  • Prior experience working in a UPI-regulated environment or with NPCI data/reporting constructs
  • Exposure to large-scale transaction data systems and familiarity with data warehousing/pipeline concepts (e.g., working with data engineering teams on instrumentation)Experience with growth, lifecycle, or CRM-driven analytics
  • Familiarity with fraud/risk analytics in a payments context

Why Join Us

  • Work on one of India's most critical financial infrastructure products — UPI — at massive scale
  • Join a high-performance analytics org embedded deeply with product and engineering, with a bias for shipping insights that matter
  • Influence decisions that affect hundreds of millions of transactions every day
  • Operate in a fast-moving, ownership-driven culture where analytics leads have real authority — and real accountability

Compensation

If you are the right fit, we believe in creating wealth for you. With 500 mn+ registered users, 25 mn+ merchants, and unmatched transaction data depth, Paytm is uniquely positioned to define the next chapter of UPI in India — and we want you to help write it.

Eligibility & process

Who can apply

See the job description for eligibility.

Skills & stack

Python SQL Next.js

Selection process

  1. As described in the employer's job posting.

Prep kit

20 resources
Study material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
Round 1: Online Assessment (OA) Aptitude, Quantitative Logic & 2 Coding Problems Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1 Core Tech Stack (Python, SQL, Next.js) & Live Code Tracing Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving Database Schemas, APIs & Architecture Basics Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion Paytm Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 5
What is the difference between WHERE and HAVING clauses in SQL? Answer
Model answer: WHERE filters rows before any groupings are applied, while HAVING filters aggregated groups after GROUP BY has executed.
Explain SQL Window functions: ROW_NUMBER(), RANK(), and DENSE_RANK(). Answer
Model answer: ROW_NUMBER() assigns unique sequential integers. RANK() assigns identical ranks to ties and skips ranks. DENSE_RANK() assigns identical ranks to ties without skipping rank numbers.
How do you handle NULL and missing values during data cleaning in Python/Pandas? Answer
Model answer: Use .isna().sum() to identify missing values. Impute with mean/median using .fillna() or remove with .dropna(subset=[...]) depending on variance impact.
What is the difference between Star Schema and Snowflake Schema in Data Warehousing? Answer
Model answer: Star Schema has denormalized dimension tables directly connected to the central Fact table. Snowflake Schema normalizes dimension tables into sub-dimensions to minimize redundancy.
How do you calculate MoM (Month-over-Month) growth in SQL? Answer
Model answer: Use LAG(revenue, 1) OVER (ORDER BY month) to fetch the previous month's revenue and compute (revenue - prev_revenue) / prev_revenue * 100.
Behavioural & HR questions 3
Why do you want to join Paytm as a Analytics Lead - UPI Product | Experience : 7-9 yrs?
Highlight Paytm's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Paytm Not disclosed
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