Lead Data Scientist

As a Lead Data Scientist, you will drive end-to-end data science initiatives that transform complex datasets into strategic insights and automated solutions with measurable business impact. You will design, deploy, and scale advanced statistical and machine learning models across critical areas, including credit risk, pricing, collections, and fraud prevention. Working at the intersection of technical leadership and business strategy, you will collaborate with cross-functional teams to integrate analytical solutions into production systems and improve decision-making. You will help establish best practices for model development, code quality, documentation, and ongoing performance monitoring. In addition to leading complex projects, you will mentor fellow data scientists and promote a culture of continuous learning and analytical rigor. This remote-first opportunity offers the chance to apply advanced technology to expand access to financing for small businesses while contributing to a collaborative, impact-driven environment.

• Professional experience: 8+ years of hands-on experience developing and deploying models using advanced statistical and machine learning techniques.

• Machine learning expertise: Strong experience with generalized linear models, gradient boosting, deep learning, and other relevant predictive modeling approaches.

• Credit risk modeling: Deep experience building and deploying credit risk models, particularly underwriting models, in fintech, lending, or financial services is highly preferred.

• Production machine learning: Experience with production-grade ML pipelines, real-time models, and automated decisioning engines is preferred.

• Programming proficiency: Expert-level knowledge of Python, SQL, and Git.

• Cloud platforms: Experience deploying and managing models in cloud environments, particularly AWS and Amazon SageMaker.

• Statistical foundations: Strong understanding of statistics, machine learning principles, and experimental design.

• Workflow orchestration: Familiarity with workflow orchestration tools such as Metaflow is preferred.

• Data infrastructure and specialized tools: Experience with cloud data warehouses such as Snowflake or Databricks, model monitoring platforms such as Arize, decision engines such as Taktile, and feature stores such as Tecton is a plus.

• Education: Bachelor's degree in Financial or Applied Mathematics, Operations Research, Economics, Statistics, or a related field. A master's degree or PhD is an advantage.

• Project management and communication: Strong organizational skills and the ability to lead complex initiatives, coordinate stakeholders, and communicate technical findings effectively.

• Critical thinking and problem-solving: Ability to evaluate complex challenges, assess trade-offs, and develop robust, practical solutions.

• Leadership and collaboration: Demonstrated ability to guide technical decisions, mentor colleagues, and work effectively across multidisciplinary teams.