Senior Data Scientist

Description About the Company Liquidity is the world's leading AI-powered private credit firm, pioneering a new standard in growth capital through a nexus of the sharpest minds in private credit and technology. With a global reach and regional expertise in every key market across North America, Europe, APAC and MENA, Liquidity supports visionary growth and mid-market companies in 45+ sectors, deploying multi-billion-dollar capital with unmatched speed, precision and adaptability. Powered by breakthrough decision science technology that deploys growth capital faster than any firm in capital markets history, Liquidity clears the path for innovative companies to move further, faster and at scale. Built on trust, Liquidity is backed by leading financial institutions including MUFG Bank Ltd., Spark Capital and KeyBank. About the Role We are looking for a Senior Data Scientist to research, architect, and deploy machine learning models and production-ready AI systems at the heart of Liquidity's credit intelligence platform. You will own the full lifecycle of data science solutions, spanning credit scoring, cash flow forecasting, and autonomous capital allocation workflows, directly shaping how we evaluate creditworthiness and deploy capital across a global portfolio. Beyond modeling, you will translate complex outputs into clear insights and present findings to credit, treasury, and investment stakeholders, closing the loop between algorithmic precision and real business decisions. This role is for highly quantitative problem-solvers who balance innovation with pragmatism, build iteratively from MVPs, and stay genuinely curious about how emerging tools can sharpen their work. You care about reliability, interpretability, and measurable impact, not just model performance in isolation. Responsibilities Credit Intelligence & Predictive Modeling: Build and deploy models for credit scoring, cash flow forecasting, risk classification, and portfolio optimization that directly inform underwriting and lending decisions. Intelligent Workflows: Design data-driven agents with financial guardrails and human-in-the-loop controls for critical treasury and capital allocation decisions. Orchestration & Multi-Agent Systems: Coordinate complex, multi-step workflows using LLM pipelines and orchestration frameworks to analyze market data, liquidity constraints, and portfolio signals. Data Storytelling & Dashboarding: Translate model outputs and portfolio insights into clear, actionable dashboards and reports for credit and executive stakeholders. MLOps & Model Lifecycle: Manage the full model lifecycle, including experiment tracking, versioning, deployment, and monitoring, to detect drift, ensure reproducibility, and maintain production reliability. Production Engineering: Develop model-serving APIs and deploy within event-driven cloud architectures, collaborating with engineering on scalable, well-observed systems. Requirements 6+ years of experience in Data Science, Quantitative Modeling, or AI/ML, including 2–4 years deploying production-grade ML models, agentic AI, or LLM pipelines. Modeling: Time-series forecasting, credit scoring, regression, classification, and optimization; proficiency with tree-based models (XGBoost, LightGBM) and model interpretability techniques such as SHAP. Technical Stack: Advanced Python (Pandas, Scikit-learn); MLOps tooling (MLflow); exposure to deep learning frameworks (PyTorch or TensorFlow) a plus. Data & Databases: Strong SQL; experience with Postgres, MySQL, or Databricks. Visualization: Fluency in at least one dashboarding tool (Streamlit, Tableau, or Power BI). AI Fluency: Stays current with the rapidly evolving AI landscape; able to translate business problems into clear AI directives and effectively supervise and evaluate intelligent workflows. Software Engineering: Strong coding practices, with the ability to write clean, maintainable Python and debug complex issues across data pipelines, model serving, and integrated systems. Communication & Presentation: Able to distill complex quantitative work into clear narratives and present findings confidently to credit, investment, and executive stakeholders. Master's degree or Ph.D. in a quantitative field (Computer Science, Statistics, Mathematics, Finance, etc.). Domain experience in FinTech, private credit, or quantitative finance. Infrastructure & deployment: Docker, CI/CD pipelines, cloud platforms (AWS Lambda, Serverless, Containers), and observability tooling (Langfuse, CloudWatch, or Datadog). NoSQL and alternative data stores: MongoDB, Neo4j, and vector databases. Familiarity with web technologies (REST APIs, basic HTML/JS) and ETL pipeline design. Experience with FastMCP or MCP-based tooling.