Senior ML Data Scientist (Credit Risk)

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Company Overview

A fast-growing AI-native financial technology company is developing a modern banking platform designed to expand access to premium financial services for young professionals in global markets. The organization combines expertise in banking, artificial intelligence, and high-growth technology to create innovative financial products and services.

The company is backed by leading technology and venture investors and is focused on building a next-generation financial platform powered by advanced artificial intelligence and machine learning.

The organization is seeking a Senior ML Data Scientist (Credit Risk) to work closely with the data leadership team across credit risk, account management, and fraud detection. The role focuses on advancing how financial data is represented, analyzed, and used in production machine learning systems.

Key Responsibilities

• Work closely with data leadership on the complete risk model suite, including credit decisioning, account management, and fraud detection.
• Own data preparation pipelines for model inputs, including the use of neural networks and large language models to represent transaction data as vector embeddings for quantitative analysis.
• Experiment with and deploy neural network and transformer-based architectures within underwriting processes.
• Build agent scaffolding and supporting harnesses within underwriting workflows for active, production-level experimentation.
• Design and deploy fraud detection models that combine rule-based systems and machine learning to identify suspicious activity in real time.
• Engineer features from structured and unstructured financial data sources.
• Develop monitoring systems to maintain model accuracy, reliability, and performance in production.
• Partner with engineering and risk operations teams to integrate model outputs into decision-making systems.
• Influence the technical direction of machine learning development and deployment.
• Establish strong technical standards and contribute to the continued development of machine learning capabilities.
• Evaluate emerging machine learning technologies and determine how they can be effectively applied to financial risk and underwriting.

Required Qualifications

• 7+ years of experience in applied machine learning or data science, with a focus on credit risk, fraud, or financial services.
• Hands-on experience with large language models, embedding models, or transformer-based architectures, including production or near-production deployment.
• Strong proficiency in Python and SQL.
• Experience with machine learning frameworks and technologies such as XGBoost, LightGBM, PyTorch, or similar tools.
• Strong feature engineering capabilities, with experience extracting meaningful signals from messy, sparse, or heterogeneous financial data.
• Solid statistical foundation covering anomaly detection, supervised classification, model calibration, and experimentation design.
• Experience building, deploying, and monitoring production machine learning models.
• Experience developing systems that identify performance degradation and concept drift.
• Familiarity with both rule-based and model-driven approaches and the ability to determine when each approach is appropriate.
• Ability to articulate machine learning system design tradeoffs and influence technical decisions.
• Strong communication and mentoring skills, including the ability to guide junior team members.
• Comfort operating as a senior machine learning technical leader within an early-stage organization.
• Ability to take ownership of problems end-to-end and establish technical standards for machine learning development.

Preferred Qualifications

• Experience working with emerging markets or data-sparse environments.
• Experience applying machine learning to complex financial datasets.
• Experience developing AI-powered underwriting or financial risk systems.
• Experience working with structured and unstructured financial data.
• Experience with production-scale neural networks, transformer models, or LLM-assisted data pipelines.

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