Head of Data Analytics & Science

Deskripsi pekerjaan

**Roles & Responsibilities** Strategic Leadership - Define and own BukuWarung's data strategy across payments, credit, fraud, and merchant growth - Embed analytics into GTM planning, channel performance (OTS, Digital, Partnerships), and executive decision-making - Be a credible partner to the management team & business heads - translating data into capital allocation and product prioritization decisions - Drive BukuWarung's roadmap toward a unified data infrastructure that consolidates all distribution channels and operations into a single internal system Credit Underwriting & BukuModal - Build proprietary MSME credit scoring models leveraging BukuWarung's payments transaction data, device usage patterns, merchant behavioral signals, and external alternative data sources - Develop thin-file and no-file underwriting approaches suited to Indonesia's informal merchant economy - Partner with BukuModal's lending team to define risk appetite, portfolio monitoring, and early warning systems for credit deterioration - Build models that improve approval rates while managing NPL - demonstrating that financial inclusion and credit discipline are not in tension Fraud, Risk & Trust - Design a near-real-time fraud detection engine across BukuWarung's payments channels: covering EDC/POS, QRIS soundboxes, and digital payment flows - Build risk models for merchant onboarding, transaction monitoring, and dispute resolution - Define intervention logic: when to flag, step up, block, or escalate - tuned to the risk tolerance and unit economics of each merchant segment - Partner with Operations to reduce manual reconciliation and investigation overhead through automated risk signals Payments & Hardware Analytics - Build analytics to track EDC and Bukupe QRIS device activation rates, transaction velocity post-activation, and device-level lifetime value - Identify leading indicators of merchant churn or device dormancy - enabling proactive field intervention before revenue is lost - Support Operations with data-driven visibility into channel performance, partner fulfillment SLAs, and logistics efficiency (Shipper and 3PL tracking, kit dispatch timelines, serial mapping accuracy) - Help build the business case for owning a Jakarta warehouse by modeling cost, control, and speed trade-offs vs. the current 3PL model GTM & Growth Analysis - Define channel-level performance metrics across different channels enabling smarter budget allocation and incentive design - Build merchant cohort and LTV models that inform acquisition targeting and retention investments - Develop experimentation infrastructure (A/B testing, synthetic controls, holdout groups) to drive evidence-based product and GTM decisions - Partner with the field sales team to instrument and improve the on-spot merchant activation journey Data Infrastructure & Governance - Architect modern data pipelines (streaming + batch) to support low-latency decisioning for fraud, credit, and real-time merchant insights - Build a data platform that makes clean, governed, accessible data a company-wide resource - Define data governance frameworks suited to Bank Indonesia regulations, OJK compliance requirements, and cross-border fintech data standards - Ensure data quality, lineage, and reliability - particularly for credit and fraud models where data errors carry direct financial consequences Team & Organization Building - Scale the data team from 5 to a high-performing organization of data engineers, ML engineers, analysts, and risk scientists - Hire for both technical depth and business acumen - people who can move from a model to a board slide - Build a culture of storytelling with data: dashboards that drive action, not just reports that get read - Create reusable frameworks and analytical tools that empower non-data teams (Ops, Finance, GTM) to self-serve on routine questions ### ** ** ### Requirements ### Must-Have - 8-12 years in data leadership, with at least 4 years in fintech (payments, lending, or fraud/risk) - Hands-on experience building credit underwriting models for thin-file or informal economy borrowers - MSME or consumer lending in emerging markets preferred - Deep expertise in fraud detection systems across digital payment channels (QR, card, wallet) - Proficiency in Python, SQL, and ML frameworks (XGBoost, LightGBM, deep learning for behavioral data) - Experience with streaming data architectures (Kafka, Flink, or Spark Streaming) for real-time decisioning - Proven ability to build and lead data teams - hiring, mentoring, and retaining strong talent - Strong executive communication: able to translate model outputs into business decisions and board-level narratives ### Nice-to-Have - Experience in Indonesia or Southeast Asia fintech, with familiarity with Bank Indonesia and OJK regulatory frameworks - Prior work on IoT or device telemetry analytics (relevant to EDC/POS and QRIS soundbox fleet management) - Exposure to agent-based or field sales distribution models and the analytics that support them - Experience deploying voice or alternative data signals in underwriting or fraud models