Xpanse was established with the goal of increasing access to home ownership for a broader audience. Since our launch, we've been dedicated to simplifying the mortgage lending ecosystem by building innovative software solutions. We view home ownership as a core component of the 'American Dream,' and our products play a key role in transforming that dream into reality.

We're seeking a highly innovative Data Scientist to lead the development of advanced Generative AI (GenAI) applications at our company. This role is ideal for someone who combines analytical skills, technical expertise, and creative vision to convert complex data into actionable insights and groundbreaking AI solutions. You will be at the helm of initiatives involving Large Language Models (LLMs), Foundation Models (FMs), Retrieval-Augmented Generation (RAG) applications, and the fine-tuning of Open LLMs, crafting AI-driven solutions to real-world challenges.

Job Requirements :

Leverage LLMs, FMs, and other AI technologies to analyze extensive datasets, develop predictive models, and uncover key insights.

Work alongside Machine Learning Engineers, business analysts, and stakeholders, ensuring data science aligns with and accelerates our business objectives.

Employ an experimentation mindset with A / B testing to validate model predictions, optimize performance, and confirm AI application reliability.

Oversee data collection, preprocessing, and management, maintaining data integrity for model training.

Keep abreast of AI, ML, and GenAI advancements to continually refine our applications and maintain our competitive edge.

Provide strategic, data-driven insights and recommendations to inform high-level business strategies.

Foster a culture of learning and innovation, sharing best practices in data science and AI with the team.  Qualifications :

Bachelor’s or master’s degree in computer science, Data Science, Statistics, or related field.

Over 5 years of experience in data science, demonstrating the application of ML and AI technologies in business contexts.

Deep expertise in statistical analysis, predictive modeling, and ML algorithms, especially with LLMs and FMs.

Proficiency in Python or R and ML frameworks like TensorFlow or PyTorch.

Extensive experience in data manipulation, analysis, and visualization with large datasets.

Knowledge of cloud computing platforms, ideally AWS, and familiarity with data processing and ML services.

Experience with MLOps practices, covering the entire lifecycle from model development to deployment and monitoring.

Exceptional problem-solving capabilities, an innovative mindset, and the ability to excel in a fast-paced environment.

Excellent communication skills, capable of translating complex data insights into clear, actionable business directives.