Director of Applied Data Science & AI

TrustEngine

2h•$124,649 - $200,787•Remote

About The Position

In today’s challenging financial landscape, the path to homeownership can feel out of reach for everyday borrowers. At TrustEngine, we believe these borrowers don't just need a loan; they need a coach. Our mission is to bridge the gap between cold data and human connection. By empowering Loan Officers with cutting-edge AI and predictive intelligence, we transform complex borrower data into highly actionable opportunities. We don't just build software—we engineer the roadmap to financial freedom in a tough market. We are looking for visionary innovators who want to leverage advanced machine learning to make a profound, tangible difference in people's lives. As the VP of Data Science, you will be the primary driving force behind the "engine" in TrustEngine. This is a highly strategic, senior Individual Contributor role focused on applied data science and AI. We value rigorous science, but our ultimate metric for success is shipped products, deployed pipelines, and real-world impact. You will shape our data science strategy by rolling up your sleeves to build the models that directly power our platform. We are looking for an autonomous, pragmatic expert who thrives in ambiguity—someone who can look at high-level business goals, independently determine what the business actually needs, and translate that into production-ready data products through tight collaboration with other engineering teams.

Requirements

The "Applied" Mindset: You care more about shipping a reliable model that delivers immediate business value than spending months perfecting a theoretical algorithm in a vacuum. You bias toward action and practical application.
Strategic Autonomy: Proven ability to take vague business challenges, ask the right questions, and architect comprehensive data science solutions from scratch as a self-directed contributor.
Deep Technical Expertise: Exceptional proficiency in Python, SQL, and core data science libraries (Pandas, Scikit-Learn, PyTorch/TensorFlow). Deep understanding of statistical analysis, classification, regression, and clustering algorithms.
Production Experience: Strong familiarity with big data processing frameworks (like Apache Spark) and the intricacies of actually deploying ML models into real-world, high-volume production pipelines.
Applied LLM Experience: Demonstrated hands-on experience working with Large Language Models. You should be intimately familiar with generating and utilizing embeddings, prompt engineering, and building LLM-based classification systems.
Mission-Driven: A genuine passion for using data for good—specifically, to help individuals navigate the complex world of personal finance and homeownership.

Responsibilities

Drive Applied Business Value: Partner directly with executive leadership to understand strategic goals and proactively identify areas where machine learning and predictive analytics can solve core business problems today.
Pragmatic Modeling & Analysis: Execute the hands-on development of predictive models using a variety of techniques (e.g., K-Means clustering for borrower segmentation, Random Forest models for predictive scoring). You know when to use a simple, fast model versus a complex, heavy one to get the job done.
Pioneer Applied LLM Initiatives: Drive our Generative AI capabilities by designing and implementing LLM-based solutions, specifically utilizing text embeddings and LLM-based classification to extract deep, actionable insights from unstructured data.
Bridge Prototyping and Production: Rapidly prototype new models and concepts in Jupyter Notebooks, but never stop there. You will seamlessly transition your work to build scalable, robust ML components within production environments like Spark.
Cross-Functional Collaboration: Work shoulder-to-shoulder with our Data Engineering and Product teams to architect, deploy, and monitor machine learning models in production data pipelines.