S #2597
Applications open
Full-Time Job
credit card bill payments growth
CRED
About the role
what will you do?
- own key input levers of the ccbp business - acquisition and resurrection of members paying credit card bills, and multi-card adoption - deep-diving on metrics and diagnosing drop-offs to course-correct performance.
- track and report on these levers daily/weekly/monthly, owning monthly planning and flagging deviations early.
- exercise judgment on when to resolve issues independently vs. escalate, and anticipate recurring problem patterns before they hit the numbers.
- own execution across the bill-pay funnel (discovery, reminders, payment, retry/failure), building preventive and reactive interventions to lift conversion.
- translate broader category strategy into concrete campaigns, experiments, and milestones you can ship and measure.
- own merchandising for ccbp on and off app - home page assets, cross-sell placements, communication plans.
- design and run campaigns (offers, cashback, reward mechanics) to drive adoption and repeat usage, optimizing spend for roI rather than just reach.
- focus on reducing friction and building payment confidence — low-cost engagement over short-term yield maximization.
- run a constant experimentation cadence (A/B/multivariate testing) to find growth inflection points, and productize what works into the core journey.
- partner with product and data science teams to build personalization models (due-date prediction, smart nudges, next-best-card-to-pay) that increase throughput.
- bring clear, data-backed narratives to cross-functional discussions (Marketing, Rewards, Product, Risk) to align on priorities and unblock execution.
What we’re looking for
- 4–7 years of experience in user growth/ category management roles, with prior ownership of high-frequency consumer transaction/payment categories or 3-5 years of experience in management consulting, investment banking, growth strategy, corporate strategy, or fintech strategy roles.
- data-driven mindset — comfortable analyzing data to form hypotheses, identify opportunities, and measure experiment success.
- track record of driving growth initiatives end-to-end (ideation → execution → measurable impact) at a consumer-first company.
- high ownership and bias for action in a fast-paced, ambiguous, agile environment.
- strong program/project management skills - able to run multiple cross-functional initiatives (product, marketing, risk, ops) simultaneously with attention to detail.
Eligibility & process
Who can apply
• 4–7 years of experience in user growth/ category management roles, with prior ownership of high-frequency consumer transaction/payment categories or 3-5 years of experience in management consulting, investment banking, growth strategy, corporate strategy, or fintech strategy roles. • data-driven mindset — comfortable analyzing data to form hypotheses, identify opportunities, and measure experiment success. • track record of driving growth initiatives end-to-end (ideation → execution → measurable impact) at a consumer-first company. • high ownership and bias for action in a fast-paced, amb
Skills & stack
Selection process
- As described in the employer's job posting.
Prep kit
20 resourcesStudy material 4
Aptitude Practice Questions & Mock Tests
Curated logical, quantitative, and verbal reasoning problems for the initial online screening round.
Company-Specific Interview Preparation Corner
Detailed interview experiences, exam formats, and previous test questions for CRED and tech roles.
Technical Placement Question Bank
High-yield coding cheat sheets, core CS fundamentals (OOP, DBMS, OS, Networks), and rapid revision guides.
Algorithm, DSA & Live Code Debugger Practice
Hands-on problem sets to improve coding speed and step-by-step memory debugging.
Company-specific guides 4
APTITUDE & LOGIC
Aptitude Practice Questions & Online Mock Tests
Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.
COMPANY GUIDE
CRED Interview Preparation Corner
Comprehensive guide covering typical interview formats, common questions, and preparation tips for CRED and off-campus tech roles.
MOCK TESTS & PAPERS
Comprehensive Interview Prep Resources & Syllabus
Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.
CODING PRACTICE
Algorithm and Data Structure Problem Set
Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.
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 (Next.js, Operations Management, Advanced Excel, Project Coordination, Reporting) & 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
CRED 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 CRED as a credit card bill payments growth?
Highlight CRED's market reputation, recent tech innovations, and how your skills in Next.js directly solve their team's objectives.
Highlight CRED's market reputation, recent tech innovations, and how your skills in Next.js 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).
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.
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Not disclosed
Bengaluru, India
• 4–7 years of experience in user growth/…
