Senior Data Analyst

InPrime Finserv; Bengaluru; Híbrido; INR 900.000 - 1.300.000; Jornada completa

Descripción de la vacante

InPrime Finserv Bangalore is seeking an analytics professional to act as the numbers owner for business, product and credit teams. You will shape what we measure for a new consumer app, ensure instrumentation, and report honestly on launch performance while owning the lending book's reporting layer.

You will collaborate with four data scientists and analysts, sit with the AVP and senior leadership, and build end-to-end analyses—from question to recommendation—without layers between you and

Formación

• 3+ years in analytics in fintech, lending, consumer internet, marketplace or ecommerce.
• Advanced SQL with window functions, CTEs, cohorts, and longitudinal data.
• Strong funnel and cohort analytics with measurable drop-offs and improvements.
• Event instrumentation and defining event taxonomy with engineering.
• Experimentation design for A/B tests or quasi-experiments with upfront success metrics.
• Dimensional modelling: grain, fact/dimension tables, surrogate keys.
• Python for analysis: pandas, numpy, statsmodels or sklearn.
• Proficiency with a BI tool; experience with QuickSight or Superset.

Responsabilidades

• Own business analytics: lead-to-disbursement funnel, productivity, yield and portfolio metrics.
• Own analytics for a new consumer app: define event taxonomy, instrumentation and funnel from day one.
• Measure adoption, retention and post-launch impact; report to leadership.
• Own dashboards in QuickSight and Superset with certified datasets and consistent metric definitions.
• Develop portfolio and risk reporting for lending, including vintage/cohort curves and collection metrics.
• Ensure data quality by tracing numbers to sources and fixing root causes.

Conocimientos

Advanced SQL

Event instrumentation

Funnel & cohort analytics

Python for analysis

BI tools (QuickSight, Superset)

Statistical judgement

Dimensional modelling

Experimental design

Problem solving

Herramientas

QuickSight

Superset

Descripción del empleo

InPrime Finserv |Bangalore | Full-time| Hybrid — 3 days/week in office

3+ years experience |Reports to AVP, Data Science & Analytics

About the role

You will be the analyst the business, product and credit teams come to when they need a number they can act on.

We are launching a new consumer app, and you will be the analyst on it from before day one — defining what we measure, making sure it gets instrumented, and telling us honestly how the launch is going. Alongside that you will own the business and portfolio reporting layer for our existing lending book.

Reporting is part of this job — you will own the dashboards the business runs on. But you are not a request queue. You will own analyses end to end — question, data, method, conclusion, recommendation — and be expected to explain and stand behind your conclusions with senior leadership and the founders.

You will report to the AVP, Data Science & Analytics and sit on a team of four data scientists and analysts. No layers in between: the person setting the analytics agenda is the person you work with directly.

What you'll own

• Business analytics: the lead-to-disbursement funnel with stage-wise drop-offs and turnaround times, branch and RM productivity, disbursement mix, yield, portfolio composition and operational bottlenecks.
• Product analytics for a new consumer app: we are building a consumer app and you will own its analytics from the ground up. Define the event taxonomy and the metrics that matter before launch, work with engineering to get the instrumentation right the first time rather than retrofitting it, build the acquisition-to-activation funnel from day one, and measure what actually happens after release — adoption, drop-off, retention, and whether each subsequent change moved anything. Very few analysts get to design a product's measurement layer from scratch; this is that job.
• Experimentation & impact measurement: determine whether product, process and policy changes actually worked, using appropriate cohorts, holdouts, matched comparisons or statistical tests. Define success metrics before launch, not after.
• Analytical data models: design fact/dimension tables, funnel and journey models, loan-level daily panels and analytical marts. You own the analytical design — grain, keys, dimensions and metric logic — and partner with data engineering on upstream data and productionisation.
• Self-serve reporting: build durable, documented dashboards in QuickSight and Superset backed by certified datasets and consistent metric definitions.
• Portfolio and risk reporting: the credit team's recurring view of the book — vintage and cohort curves, DPD migration, bounce and collection efficiency, approval rates and segment-level trends. You will own the reporting layer for this and partner with the data scientists on the deeper risk modelling. You do not need prior credit risk experience; you do need to be willing to learn the domain properly, because every metric here has a regulatory definition behind it.
• Data quality: notice when a number looks wrong, trace it to source and get the underlying issue fixed rather than patching individual queries.

Data agent semantic layer

We have built an internal LLM-based data agent so the business can self-serve ad-hoc questions. You will own the analytical layer it reasons over — certified datasets, valid join paths, metric definitions and business terminology — test it against real questions, investigate wrong answers and improve the underlying semantics.

You do not need prior LLM experience. We would rather hire strong analytical judgement and teach the LLM side.

The scope above is broad by design, and we have a view on sequence:

• Own the consumer app's measurement layer through launch — event taxonomy, instrumentation review with engineering, the funnel and retention views leadership will watch daily, and an honest post-launch read.
• Own the business and funnel reporting layer for the lending book — lead-to-disbursement funnel, drop-offs and TATs, branch and RM productivity, disbursement mix.
• Build the measurement discipline. Every meaningful product or process change gets a defined success metric before launch and an honest read after.
• Take on the portfolio and risk reporting layer, and the data agent's semantic layer — certified datasets, consistent definitions, and a shrinking ad-hoc queue.

Deeper risk and credit policy analytics build from there, alongside the data scientists. We will revisit this with you in the first month rather than hand it down.

What you need

• 3+ years in analytics — fintech, lending, consumer internet, marketplace or e-commerce. Lending experience is a plus, not a requirement.
• Advanced SQL: window functions, complex CTEs, temporal logic, cohorts and longitudinal/panel data.
• Funnel and cohort analytics done properly: you have found where users drop, quantified it, and had something change as a result.
• Event instrumentation: you have defined an event taxonomy and worked with engineers to get it implemented — or can convincingly show how you would. Analysts who have only consumed someone else's events will find the app work hard.
• Experimentation: you can design an A/B test or quasi-experiment, define the success metric up front, and read the result honestly — including when it says the change did nothing.
• Dimensional modelling: grain, fact/dimension tables, surrogate keys, slowly changing dimensions and additivity.
• Python for analysis: pandas, numpy and basic statsmodels/sklearn.
• Strong experience with a BI tool. We use QuickSight and Superset; Tableau, Power BI, Looker, Metabase etc. transfer fine.
• Statistical judgement: cohort maturity, confidence intervals, mix effects, and why before/after movement alone does not establish impact.
• Rigour about definitions and clarity in writing. You should be irritated by two dashboards showing different numbers for the same metric and fix it at the definition, not in the query. And you should be able to take a loosely defined business question, turn it into a testable one, and write up the answer in a page.

Nice to have

• Product analytics tooling — Mixpanel, Amplitude, CleverTap, Firebase
• Having analysed a consumer app through its first year
• AWS analytics stack — Athena, Glue, S3, Bedrock
• dbt or similar transformation frameworks
• Credit risk exposure — DPD, vintage, roll rates, scorecards, ECL / Ind AS 109

How we work

Small team, high autonomy, low process overhead. Based in Bangalore, 3 days per week from office. You will work directly with credit, product, engineering, compliance and the founders, and day to day with the data scientists on the team.

Your analysis will change what gets shipped — which means we would rather you take an extra day and be right than ship fast and be wrong.