Machine Learning Engineer - Financial Data

As a Machine Learning Engineer on Clair’s Data team, you will be responsible for transforming diverse financial data sources, including but not limited to Plaid transactions, into production-grade features, predictive signals, and modeling pipelines that power Clair’s credit and underwriting systems.

Your work will focus on building the core feature layer that our credit models depend on: extracting structure from messy financial data, designing robust predictive features, forecasting key user-level events (such as income timing or financial stability indicators), and ensuring that these features are efficiently deployed, monitored, and continuously improved.

In addition to hands-on modeling and feature engineering, you will contribute to the development of Clair’s Data Science platform – building tooling, automation, and infrastructure that make it easier to train, deploy, and monitor ML systems at scale.

This role is ideal for someone who loves crafting meaningful features out of raw data, scaling ML systems in production, and driving the technical foundations of a modern financial ML stack.

• Engineer high-quality features from financial and behavioral data to support credit modeling.
• Build and maintain scalable pipelines for feature computation, income detection, and financial signal extraction.
• Develop forecasting and predictive models for income timing, user stability, and financial behavior patterns.
• Deploy, monitor, and optimize ML models and feature pipelines in production using AWS, Snowflake, or other tooling.
• Contribute to the development of Clair’s Data Science platform, including model deployment workflows, feature stores, and monitoring systems.
• Collaborate with engineering, product, and risk teams to understand their workflows and ensure data products have high availability for their use cases
• Prototype new modeling techniques and evaluate their impact on credit and underwriting performance.
• Communicate findings and technical decisions clearly to both technical and non-technical stakeholders.

• 5+ years of experience in machine learning engineering, data science, data engineering,  or a related technical field.
• Strong experience in feature engineering, particularly with large-scale transactional or time-series datasets.
• Proficiency in Python, SQL, and modern ML frameworks (e.g., PyTorch, XGBoost, sci-kit learn).
• Experience building production ML pipelines and deploying models in cloud environments 
• Strong understanding of data modeling, forecasting, and statistical analysis.
• Clear and effective communication skills when working with product, engineering, and business stakeholders.
• Demonstrated ability to execute end-to-end: from raw data exploration to production ML implementation.
• Software engineering proficiency with a sensible adherence to good software design patterns 

• Experience with Plaid or similar financial data providers.
• Background in credit risk modeling, underwriting features, or financial signal extraction.
• Familiarity with MLOps tools (e.g., MLflow, SageMaker, Feature Stores, Airflow).
• Experience building or contributing to internal Data Science platforms.
• Knowledge of explainable ML techniques and regulated-model requirements.
• Prior work in fintech, credit, or payments.

Location: This is a hybrid position based out of New York City, you will be expected to come into the office at least three days a week (Tuesdays, Wednesdays, & Thursdays) with additional days on occasion for client meetings. We’re open to remote candidates whose experience and background strongly match the requirements of the role.

Compensation: The annual base salary for this role is $200,000 for NYC and SF-based candidates. The base pay for this role is determined using many factors, such as education, skills and experience and is reflective of Clair Series stage and size.  Base pay is only one part of Clair’s competitive total compensation package which includes equity, benefits and additional perks. The base pay range is subject to change and may be modified in the future.

Clair will only contact candidates from @getclair.com email addresses. We will never ask for payments or sensitive personal information during the hiring process. If you happen to receive anything suspicious, please ignore it.