Data Analyst, Collections & Lending Analytics (Delivery)

About Gnani.ai and the team

Gnani.ai builds proprietary voice and conversational AI – ASR (Automatic Speech Recognition), TTS (Text-to-Speech), SLMs (Small Language Models) and agentic voice platforms such as Inya.ai – across 22+ Indian languages.

• The Delivery team runs AI voice and messaging campaigns for leading Indian banks, NBFCs (Non-Banking Financial Companies) and retail lenders.
• Collections portfolios: Personal Loan, Business Loan, Auto Loan and Gold Loan, across Pre-due, Post-due (Self-cure), Charges Collection and Settlement buckets.
• Sales portfolios: lead qualification for SME (Small and Medium Enterprise) loans, Top-up loans and Refinance/Balance Transfer.
• You will sit at the center of this engine: your segmentation and analysis decide who gets called, when, and with what message – and the insights you produce decide how much our clients collect and convert. Role purpose

This role exists to turn campaign, call-outcome and payment data into strategies that measurably lift collection efficiency and lead conversion for our clients. File uploads and campaign operations sit with a separate owner; this role is purely analytical.

• Insight: clients see clear, trusted dashboards and understand why numbers moved, not just that they moved.
• Uplift: tested calling and messaging strategies that move connectivity, PTP (Promise to Pay) kept rate and collection efficiency month on month.
• Proof: every claimed improvement is measured against a control group, so clients trust the impact attributed to Gnani.
• Account growth: data-backed evidence that helps Delivery expand scope and revenue within each account. Key responsibilities

1. Data quality and segmentation logic

• Define segmentation rules (bucket, DPD – Days Past Due, ticket size, language, past behaviour) that the campaign operations owner executes.
• Specify suppression logic – DND (Do Not Disturb), opt-outs, paid-customer exclusion – and audit that it is applied correctly.
• Audit data quality weekly: dialed vs. allocated base, disposition consistency, payment-file match rates; flag gaps to the Project Manager.
• Own the data dictionary for each account so metrics are calculated the same way every time. 2. Data logic and analysis
• Build the full funnel per account: dialed → connected → RPC (Right Party Contact) → engaged → PTP → paid.
• Find drivers of drop-off by segment, time slot, language, retry count and script variant.
• Use Mongo and Python to analyze large call and payment datasets and automate recurring analysis.
• Build simple propensity views (who is likely to pay or respond) to prioritize calling effort. 3. Dashboards and reporting
• Build and maintain client dashboards (Power BI or Excel) for daily, weekly and monthly reviews.
• Present data in a clear, client-ready format: trends, bucket-wise and product-wise performance, and next actions.
• Prepare data packs for QBRs (Quarterly Business Reviews) with the Project Manager. 4. Calling and messaging strategy
• Design segment-wise strategies: time-of-day slots, retry cadence, language, channel mix (voice, WhatsApp, SMS – Short Message Service) and script variants.
• Run A/B tests and measure uplift against a control group before scaling a change.
• Recommend bucket-specific approaches (e.g., reminder-led for Pre-due, PTP-led for Self-cure, offer-led for Settlement). 5. Client communication
• Lead analytical conversations with client collections, risk and product teams: performance reviews, strategy proposals, test readouts.
• Explain numbers in business terms (amount recovered, roll-back avoided) rather than call counts.
• Manage multiple accounts in parallel with clear prioritization agreed with the Project Manager. 6. Data protection and compliance
• Handle borrower data (names, phone numbers, loan account numbers, outstanding amounts) as sensitive PII (Personally Identifiable Information).
• Follow the DPDP Act (Digital Personal Data Protection Act, 2023), RBI (Reserve Bank of India) Fair Practices Code and recovery-agent guidelines, and TRAI (Telecom Regulatory Authority of India) calling-hour and DND rules.
• Use only approved, access-controlled storage; never share client data over personal email or messaging apps. Must-have skills and qualifications
• Bachelor's degree in Statistics, Mathematics, Economics, Engineering, Commerce or a related field.
• 2–4 years in collections analytics, lending/risk analytics or campaign analytics at a bank, NBFC, fintech or collections agency or running campaigns for an Indian Voice AI Startup
• Advanced Excel: XLOOKUP/VLOOKUP, INDEX-MATCH, pivot tables, IF/IFS, COUNTIFS/SUMIFS, Power Query.
• Strong Database Skills: JOINs, GROUP BY, CASE WHEN, CTEs (Common Table Expressions), window functions.
• Working Python (pandas) for analysis and automating recurring reports.
• Applied statistics: control groups, significance testing, uplift measurement.
• Dashboarding in Power BI with clear visual storytelling for business audiences.
• Preferred: Working knowledge of collections metrics and bucket logic (see Domain knowledge).
• Clear written and spoken English; Hindi or another Indian language is a plus for client calls. Good-to-have skills
• Experience with dialer, CRM (Customer Relationship Management) or LMS (Loan Management System) data exports.
• Propensity or risk scoring (e.g., logistic regression, decision trees) to prioritize who to call and when.
• Exposure to voice AI or contact-center analytics (call dispositions, AHT – Average Handle Time, sentiment).
• Workflow automation: scheduled scripts, SFTP (Secure File Transfer Protocol) or API (Application Programming Interface) based file transfers.
• Lead-funnel analytics for loan sales: lead-to-qualified, qualified-to-disbursal conversion.