Jobs at Tala

The Senior Product Analyst shapes product direction through rigorous analysis, product intuition, and ownership of metrics, instrumentation, and experimentation. Responsibilities include developing hypotheses, conducting root-cause analysis, designing tracking plans, implementing monitoring and alerts, and leading A/B and multivariate experiments. The role requires translating complex analysis into product decisions, partnering with Product, Engineering, and Design, and applying advanced statistical methods to identify growth opportunities and product issues.

Owns credit risk strategy and portfolio performance for Tala’s expansion markets, including direct-to-consumer and partnership lending. Responsibilities include monitoring portfolio KPIs, calibrating approval policies, setting limits and pricing, developing underwriting strategies, evaluating partner data and risk economics, and collaborating with Product and Data Science on decisioning improvements. The role also communicates portfolio insights to leadership and influences cross-functional stakeholders in a global, fast-paced environment.

Designs, develops, and operates scalable backend services, APIs, data integrations, and microservices. Owns domain architecture, database performance, reliability, observability, incident response, security, testing, and roadmap delivery. Leads design reviews, improves engineering standards, mentors backend engineers, and collaborates with product and global teams. The role requires technical leadership, strong system design, cloud infrastructure experience, and production operation of large-scale consumer applications.

Own Tala’s product strategy and roadmap for fraud prevention, authentication, identity, and risk capabilities. Partner with Engineering, Data Science, Analytics, and business teams to build data-driven fraud products, define decisioning metrics and thresholds, manage fraud incidents, improve customer security, and minimize friction. Lead cross-functional execution from discovery through launch and iteration while managing external technology partners.

Leads Tala’s Machine Learning Engineering team in building and operating platforms for model development, real-time inference, streaming features, batch processing, deployment, monitoring, and production reliability. Manages and develops 4–6 engineers, owns delivery and prioritization, provides architectural leadership, improves engineering practices and SLOs, and partners with Data Science, Data Engineering, Product, Credit, and Business Development teams.

Manage and elevate Tala’s global visual brand identity, including design systems, guidelines, sub-brand architectures, and brand integrity. Create digital marketing, campaign, social, email, illustration, animation, and video assets across online and offline channels. Use performance insights to improve creative, collaborate with marketing and strategy teams, manage multiple priorities independently, and maintain consistent, high-quality visual experiences across cultures and touchpoints.

Own the architecture and hands-on development of Tala’s shared data and analytics platforms. Build semantic layers, governed business metrics, data models, pipelines, services, tooling, and AI-enabled analytics workflows. Lead cross-functional technical initiatives spanning Product, Engineering, Analytics, Finance, and Accounting; improve governance, reliability, usability, and documentation; resolve architectural bottlenecks; and mentor engineers through technical leadership.

Design, implement, and automate cloud-native infrastructure and application security on AWS/GCP. Build defense-in-depth controls, hardening, vulnerability management, detection, response, and container/Kubernetes security. Collaborate with Product, DevOps, and SREs; support incident triage and use automation (including Python) and AI to optimize SecOps.

Own credit strategy and performance for expansion and partnership portfolios. Monitor KPIs, design approval/pricing/limit policies, lead partnership underwriting, translate data into decisioning improvements, and collaborate with Product and Data Science to implement risk strategies and pilot evaluations.