Senior Product Analytics

About Aspora

People on the move deserve a bank that moves with them. Since 2022, Aspora has been building a borderless financial operating system that makes money as mobile and transparent as its users.

Backed by influential venture capitalists like Sequoia Capital, Greylock Partners, Hummingbird Ventures, Y Combinator & Global Founders Capital. We're a team of 75+ across India, the UK, the UAE, EU and the US, working with extreme ownership, radical candour, and an obsession with customer impact.

We celebrate builders who question assumptions, ship fast, and turn regulatory complexity into elegant solutions. If you’re driven to redefine what global banking can be, we’d love to build the future with you.

Senior Product Analyst

Team: Analytics |

Location: Bengaluru, India

The Role

We’re hiring Senior Product Analysts across our four product verticals: Core Remittance & BBPS, Remittance Experience, Banking (NRE/NRO, Fixed Deposits), and Wealth (Digital Gold, Global Equities). You’ll be embedded with a product team as their dedicated analytics partner, but you’ll also contribute to cross-cutting initiatives that shape Aspora’s entire analytics capability.

This is not a dashboarding role. You’ll be the person in the room who changes a product decision because you found something no one else saw. You’ll build the measurement frameworks for products that are scaling from zero to millions of users. You’ll design experiments where a 1% conversion improvement translates to millions in incremental volume. And you’ll do it at a company where data infrastructure is still being built — which means you’ll get your hands dirty, not just consume clean datasets.

What You Will Do

Own product analytics end-to-end for your vertical. You will define the metrics that matter, build the dashboards that the team checks daily, and surface the insights that drive roadmap decisions. For remittance, this means transaction analytics across corridors, success/failure analysis, settlement tracking, and pricing optimization. For banking, this means building the analytics layer for NRE/NRO accounts and fixed deposits from scratch — defining metrics, instrumentation requirements, and baselines. For wealth, this means analyzing investment behavior: buy/sell flows, pricing and spread performance, ticket sizes, frequency, and portfolio patterns.

Build and own activation and conversion funnels. The gap between KYC completion and first transaction is where Aspora destroys the most value today. You will map this funnel granularly by geography, channel, device, and corridor. You will identify the “activation moment” — the specific early behavior that best predicts long-term retention — and validate it with data. You will target a 15–25% improvement in first-transaction rate through data-driven experiments.

Drive retention and churn analytics. You will build cohort-level retention curves, develop churn prediction capabilities that identify at-risk users 30 days out, and design the interventions to re-engage them. With 500K+ active users, even small retention improvements translate to tens of millions in incremental volume.

Build cross-sell and propensity models. You will quantify the cross-sell opportunity from remittance users into banking, wealth, and BBPS products. You will build propensity models that feed into the Next Best Action engine to target the right user with the right product at the right time.

Conduct corridor-level and product-level P&L analysis. You will analyze revenue, margins, FX spreads, fees, and unit economics at the corridor and product level. At $6B+ volume, even 2–3 bps of average spread improvement = $800K–$1.2M in incremental annual revenue. You will find these opportunities.

Design and measure experiments with statistical rigor. At Aspora’s transaction volume, sloppy A/B tests are expensive. You will design experiments with proper sample sizing, run significance tests, and communicate results that drive decisions — not just statistical outputs.

Contribute to cross-cutting analytics initiatives. You will help build the user segmentation model that the entire org uses, contribute to the experimentation infrastructure, support the Next Best Action framework, and help establish automated monitoring agents that surface anomalies before humans notice them.

Ensure analytics readiness for new product launches. You will define metrics, document event instrumentation requirements, establish baselines, and build the measurement plan before a product launches — not after.

Must Have

SQL — Advanced, daily-driver level. Window functions, CTEs, complex multi-table joins, subqueries, query optimization. You can trace data from raw event to final dashboard number and find where it breaks. You’re comfortable with datasets at the scale of millions to billions of rows. You don’t wait for data engineering to build a view — you write the query yourself and validate the logic.

Product analytics depth. You have deep, hands-on experience with the core toolkit: funnel analysis (building multi-step funnels, identifying drop-off points, measuring conversion by segment), cohort analysis (retention curves, behavioral cohorts, not just acquisition cohorts), experimentation (A/B test design, sample size calculation, significance testing, understanding of common pitfalls like novelty effects, peeking, and Simpson’s paradox), and segmentation (behavioral segmentation, RFM, engagement scoring, value-based segments).

BI tool expertise. You are expert-level in at least one of: Looker, Tableau, Metabase, Power BI, or Superset. You build production-grade, self-serve dashboards that stakeholders actually use daily — not one-off charts. You understand data modeling for BI (star schemas, measures vs. dimensions).

Financial data literacy. You can work with transactional financial data: amounts, currencies, exchange rates, fees, spreads, margins, and reconciliation. You can calculate unit economics (revenue per transaction, cost to serve, CAC, LTV, contribution margin) and explain what they mean for the business. You understand what P&L analysis means at a product level.

Stakeholder communication. You can translate complex data findings into clear, actionable recommendations for non-technical stakeholders. You don’t just show dashboards — you tell the story behind the numbers. You write clearly, present confidently, and can hold your own in a room with product managers, engineers, and executives.

AI fluency. You actively use AI tools (ChatGPT, Claude, GitHub Copilot, Cursor, or similar) to accelerate your analytical workflow — writing SQL faster, generating hypotheses, debugging code, summarizing findings, or automating repetitive tasks. You see AI as a force multiplier that makes you 2–3x more productive, not a novelty.

Python or R for analysis. You go beyond SQL when needed — statistical modeling, data wrangling with pandas, predictive modeling with scikit-learn, or building lightweight scripts for automation. You can build a churn prediction model, not just consume one.

5+ years of experience in product analytics, business analytics, or data analytics at a fintech, payments, or high-growth consumer tech company. You’ve been the primary analytics partner for a product team — not a member of a centralized reporting team that fulfills tickets.

Good to Have

Cross-border payments or remittance domain knowledge. You understand corridors, payout methods, FX mechanics, settlement cycles, success/failure rates, and compliance requirements. You know how money actually moves across borders and where data quality issues typically emerge.

Event tracking platform experience. You’ve worked with Mixpanel, Amplitude, Segment, or similar. You’ve built tracking plans, debugged event schemas, and know the difference between a well-instrumented product and a poorly-instrumented one.

dbt or analytics engineering experience. You’ve worked with the modern data stack and understand how raw data becomes analytics-ready through transformation layers. You can write dbt models, not just query the output.

Experience with 0-to-1 product launches. You’ve built the analytics layer for a product from scratch — defined metrics before launch, worked with engineering on instrumentation, and measured product-market fit.

Pricing analytics or FX optimization experience. You’ve worked on pricing elasticity, dynamic pricing, spread optimization, or fee structure experimentation.

NRI banking or investment product knowledge. You understand NRE/NRO accounts, FEMA regulations, digital gold/silver mechanics, or global equity platforms.

Automated monitoring or alerting experience. You’ve built or contributed to systems that proactively detect anomalies (volume drops, conversion dips, revenue shifts) without human intervention.

Multi-geography experience. You’ve worked with data across multiple countries, currencies, and regulatory environments simultaneously.