Senior Data Scientist

Company Overview

Liquidity leads as a premier AI-driven private credit firm, setting new benchmarks in growth capital by integrating cutting-edge technology with top experts in private credit. Operating worldwide with a strong presence in North America, Europe, APAC, and MENA, Liquidity supports over 45 industry sectors. It manages multi-billion-dollar investments with exceptional agility and precision, thanks to advanced decision science technology that expedites capital deployment like never before. Trusted by major financial institutions such as MUFG Bank Ltd., Spark Capital, and KeyBank, Liquidity empowers innovative companies to expand efficiently and at scale.

Role Summary

We seek a Senior Data Scientist to lead the development, implementation, and maintenance of machine learning models and AI solutions crucial to our credit intelligence platform. This role involves end-to-end ownership of projects including credit scoring, cash flow prediction, and automated capital allocation processes, directly influencing credit evaluation and capital deployment worldwide. The successful candidate will also communicate complex statistical outcomes clearly to credit, treasury, and investment teams to bridge advanced analytics with strategic business decisions. Ideal candidates are highly analytical, pragmatic innovators who thrive on iterative development and are keenly interested in leveraging emerging AI technologies to enhance impact, reliability, and interpretability beyond mere model accuracy.

Key Responsibilities

• Develop and implement credit intelligence and predictive models such as credit scoring, risk assessment, cash flow forecasting, and portfolio optimization to guide underwriting and lending.
• Design intelligent, data-driven workflows incorporating financial controls and human oversight for treasury and capital allocation functions.
• Coordinate multi-step, agent-based systems using large language model pipelines and orchestration tools to analyze market conditions and portfolio metrics.
• Create clear and actionable dashboards and reports to translate model results and portfolio insights for credit and executive stakeholders.
• Manage the entire ML model lifecycle including experiment tracking, version control, deployment, and continuous monitoring to uphold model integrity and reliability.
• Collaborate on engineering efforts to build scalable APIs for real-time model deployment within event-driven cloud infrastructures.

Required Qualifications

• Minimum of 6 years in data science, quantitative analytics, or AI/ML roles, including 2 to 4 years with production-level ML deployments or experience in agentic AI and large language model orchestration.
• Expertise in time-series forecasting, credit risk scoring, regression, classification, and optimization methods; skilled with tree-based algorithms like XGBoost and LightGBM, plus model explanation tools such as SHAP.
• Proficient in Python programming, particularly Pandas and Scikit-learn; experience with ML lifecycle tools like MLflow; familiarity with deep learning frameworks like PyTorch or TensorFlow is a plus.
• Strong SQL skills and working knowledge of databases such as Postgres, MySQL, or Databricks.
• Experience with visualization software such as Streamlit, Tableau, or Power BI for effective data storytelling.
• Awareness of evolving AI trends and ability to translate business challenges into AI-driven solutions with effective supervision of intelligent workflows.
• Solid software engineering skills, writing clean, maintainable Python code, troubleshooting, and debugging complex data and model pipelines.
• Excellent communication skills to clearly explain technical results and influence credit, investment, and executive decision-making.

Preferred Qualifications

• Master’s or Ph.D. in Computer Science, Statistics, Mathematics, Finance, or related quantitative disciplines.
• Experience in FinTech, private credit, or quantitative finance sectors.
• Familiarity with deployment infrastructure technologies such as Docker, CI/CD workflows, cloud services (AWS Lambda, serverless, containers), and monitoring tools like Langfuse, CloudWatch, or Datadog.
• Knowledge of NoSQL and alternative databases including MongoDB, Neo4j, and vector stores.
• Skills in web technologies (REST APIs, basic HTML/JavaScript) and ETL pipeline construction.
• Experience with FastMCP or Model-Centric Programming tools.

Equal Opportunity

Liquidity is committed to providing equal opportunities and fostering diversity and inclusion within the workplace.