Senior Data Scientist

We process transactions worth $80B annually and are trusted by more than 800,000 businesses and are one of the first entities to receive critical licenses like PA-PG, PA-CB, and PPI in the sector. We enable seamless collections, payouts, identity verification, and fraud detection. Backed by the world's leading investors.

As a Senior Data Scientist, you will play a pivotal role in shaping the future of payments by defining and executing the data science roadmap for various business domains. This role involves building impactful solutions, providing technical leadership, and collaborating with cross-functional teams to deliver data-driven insights and innovation.

• Partner with stakeholders to define, own, and execute the data science roadmap for one or more business domains, building impactful solutions from the ground up
• Design, develop, and deploy classical ML and statistical models into production environments, with hands-on ownership through the deployment stack (not just the modeling stage)
• Work with engineering-adjacent tooling such as Kafka, Redis, Terraform, and AWS to take models from prototype to reliable production systems — you don’t need to be a software engineer, but you do need to understand this stack well enough to work through deployment issues independently
• Identify and lead applied AI/GenAI integration opportunities where they add clear business value, while keeping the classical ML and engineering fundamentals central to the role
• Provide strong technical and scientific leadership, mentoring team members and driving best practices
• Establish, standardize, and continuously improve data science and engineering practices within the team
• Balance business delivery with innovation by contributing to applied research and experimentation

• 5+ years of experience applying machine learning and statistical methods to solve complex, large-scale business problems, ideally from a product-based company with a strong engineering culture
• Deep understanding of classical ML techniques and their mathematical foundations — this is the core of the role, not a secondary skill
• Demonstrated, hands-on experience deploying ML models to production, with real exposure to tools such as Kafka, Redis, Terraform, and AWS
• Comfortable working on applied AI/GenAI use cases as they come up, without that being the primary skill set
• Strong expertise in model evaluation techniques and the ability to align model performance with business objectives
• Experience working with scalable and distributed systems
• Proven ability to translate business problems into data science solutions

• comprehensive array of employee perks and wellness initiatives

• Stage 1: Apply and submit the application form
• Stage 2: Attempt AI interview (15 min)
• Stage 3: Round 1: Technical — hands-on Python & SQL
• Stage 4: Round 2: Technical — hands-on Python & SQL
• Stage 5: Round 3: Hiring Manager round — deep dive on past projects, with a focus on ML deployment and engineering depth