Summary of the role
We’re looking for a detail-oriented data analyst who enjoys turning raw data into clear, well-structured reports. Someone who takes pride in accuracy and presentation, is comfortable working to recurring deadlines across multiple clients, and is keen to grow their skills as part of a small, collaborative international team building things from scratch.
Key Responsibilities
- Produce portfolio risk assessments for Payment Facilitators joining our largest client's network: Each PayFac brings a portfolio of hundreds or thousands of sub-merchants, and you'll profile it end to end - transaction and authorisation volumes, chargeback and refund behaviour, industry and geographic concentration, and merchant-level outliers - to give the client a clear view of the risk they're taking on.
- Screen sub-merchant websites across those portfolios against a structured risk framework: confirming the business is genuine and trading as described, that terms, refund policies, and contact details meet card scheme requirements, and that there are no sanctions, reputational, or fraud signals. Findings feed directly into the client's risk decisions.
- Build quarterly portfolio reviews for our largest client: comparing risk performance across the PayFacs in their network - dispute and fraud rates, merchant churn, portfolio mix, and emerging concentrations - and turning that into a clear, well-designed deck our risk-team presents to leadership.
- Answer ad hoc analytical questions from across the business: sizing a segment, investigating a cluster of merchants behaving unusually, or tracking down data quality problems in an incoming PayFac feed.
- Work with our Product team on the client-facing risk platform: specifying the data and metrics new features need, testing them against real portfolio data, and validating outputs before release.
Role Requirements
- Bachelor's degree in a quantitative field (Statistics, Economics, Computer Science, Engineering, Mathematics, or similar). Candidates from IITs, NITs, BITS, ISI, or comparable institutions are particularly encouraged to apply.
- 2–5 years' experience in a data analyst, business intelligence, or a reporting role including at least one role where you owned recurring stakeholder-facing reporting end to end. Fintech, payments, or risk/fraud environments are preferred but not essential.
- Strong SQL - comfortable writing and optimising queries against large tables (joins, aggregations, window functions, CTEs). You should be able to take an analytical question and get to the data yourself without hand-holding.
- Working proficiency in Python for analysis and automation - pandas, file handling, and scripting repeatable reporting tasks rather than redoing them by hand each cycle.
- Comfortable working with spreadsheets and building clear, structured slide decks (PowerPoint/Google Slides) from data.
- High attention to detail - this role involves compliance-adjacent and client-facing outputs where accuracy matters.
- Good written communication for report and deck narratives.
Nice-to-Haves
- Experience with Databricks or PySpark, or another cloud data platform (Snowflake, BigQuery). We work in Databricks - if you're strong in SQL and Python, we'll get you up to speed.
- Prior experience working with international or remote teams, and client-facing roles.
- Interest in progressing toward data science work as the role develops.
Perks
- Flexible, remote-first working arrangements.
- Opportunities for significant professional growth and advancement within a company at the forefront of transforming risk management in payments.
- Globally-focused company, with exposure to Asian, European and American markets.
- A competitive compensation package, including company equity and performance incentives.
- Private healthcare employee insurance.
Join Us!
At Envisso, we are committed to fostering a collaborative, supportive, and inclusive environment. If you are ready to play a crucial role in shaping the future of payment risk management, we would love to hear from you! We particularly welcome applications from women.