Manager, Data Scientist and Machine Learning Engineer

As a Data Scientist at our company, you will be responsible for supporting the development and deployment of machine learning models and analytics solutions aimed at improving decision-making across the mortgage lifecycle. Your key responsibilities will include: - Developing and maintaining machine learning models and statistical tools for various use cases such as risk scoring, churn prediction, segmentation, and document classification. - Collaborating with Product, Engineering, and Analytics teams to identify data-driven opportunities and support automation initiatives. - Translating business questions into modeling tasks and contributing to the design of experiments and success metrics. - Assisting in building and maintaining data pipelines and model deployment workflows in partnership with data engineering. - Applying techniques such as supervised learning, clustering, and basic NLP to structured and semi-structured mortgage data. - Supporting model monitoring, performance tracking, and documentation to ensure compliance and audit readiness. - Contributing to internal best practices and participating in peer reviews and knowledge-sharing sessions. - Staying current with developments in machine learning and analytics relevant to mortgage and financial services. Qualifications required for this role include: - Minimum education required: Masters or PhD in engineering/math/statistics/economics, or a related field - Minimum years of experience required: 2 (or 1, post-PhD), ideally in mortgage, fintech, or financial services - Required certifications: None - Experience working with structured and semi-structured data; exposure to NLP or document classification is a plus. - Understanding of model development lifecycle, including training, validation, and deployment. - Familiarity with data privacy and compliance considerations (e.g., ECOA, CCPA, GDPR) is preferred. - Strong communication skills and ability to present findings to technical and non-technical audiences. - Proficiency in Python (e.g., scikit-learn, pandas), SQL, and familiarity with ML frameworks like TensorFlow or PyTorch.,