• 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. • Pioneer Applied LLM Initiatives: Drive our Generative AI capabilities by designing and implementing LLM-based solutions. • Bridge Prototyping and Production: Rapidly prototype new models and concepts and 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.

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. The Role As the Director of Applied Data Science & AI, 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. What You’ll Do - 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, 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.

• Shape the intersection of product, data, and customer experience at Daloopa. • Own the analytics foundation that powers product strategy. • Build dashboards, define metrics, synthesize customer behavior. • Ensure teams have the clarity they need to move quickly. • Aggregate and analyze qualitative data from various sources. • Identify themes and translate user feedback into actionable product insights. • Maintain analytics documentation and dashboards.

• Build and lead a high-performing data science team, fostering collaboration and innovation. • Mentor and guide team members on technical and strategic initiatives. • Design and implement advanced machine learning models for fraud detection, risk scoring, and acceptance optimization. • Collaborate with engineering and risk teams to integrate models into production systems. • Define and execute a scalable data science strategy that aligns with Conekta's business objectives. • Develop frameworks for data collection, feature engineering, and real-time model deployment. • Analyze large-scale payment data to identify trends, anomalies, and areas for improvement in payment success rates. • Develop predictive models to enhance payment authorization rates and reduce transaction declines. • Continuously refine fraud detection models to address emerging threats and new fraud patterns. • Partner with product, engineering, and business teams to align data science initiatives with company goals. • Communicate complex findings and insights to non-technical stakeholders effectively