Senior Manager, Data Science and Analytics

Role Overview: You are seeking a Data Scientist to support the development and deployment of machine learning models and analytics solutions aimed at enhancing decision-making throughout the mortgage lifecycle. In this hands-on role, you will contribute to building predictive models, customer segmentation tools, and automation workflows to improve operational efficiency and customer outcomes. Your work will involve collaborating closely with senior data scientists and cross-functional teams to translate business requirements into well-defined modeling tasks, with opportunities to utilize natural language processing (NLP), statistical modeling, and experimentation frameworks in a regulated financial setting. You will report to a senior leader in Data Science. Key Responsibilities: - Develop and maintain machine learning models and statistical tools for various use cases like risk scoring, churn prediction, segmentation, and document classification. - Collaborate with Product, Engineering, and Analytics teams to identify data-driven opportunities and support automation initiatives. - Translate business questions into modeling tasks, contribute to experiment design, and define success metrics. - Assist in constructing and managing data pipelines and model deployment workflows in collaboration with data engineering. - Apply techniques such as supervised learning, clustering, and basic NLP to structured and semi-structured mortgage data. - Support model monitoring, performance tracking, and documentation to ensure compliance and audit readiness. - Contribute to internal best practices, participate in peer reviews, and engage in knowledge-sharing sessions. - Stay updated with advancements in machine learning and analytics relevant to mortgage and financial services. Qualifications: - Minimum education required: Masters or PhD in engineering, math, statistics, economics, or related fields. - Minimum years of experience required: 2 (or 1 post-PhD), preferably in mortgage, fintech, or financial services. - Required certifications: None - Specific skill or ability needed: Experience working with structured and semi-structured data; exposure to NLP or document classification is advantageous. Understanding of the 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 the ability to present findings to both technical and non-technical audiences. - Minimum software or applications experience required/preferred: Proficiency in Python (e.g., scikit-learn, pandas), SQL, and familiarity with ML frameworks like TensorFlow or PyTorch. - Minimum experience required using mobile technology: None (Note: The additional details about the company provided in the job description have been omitted in this summary.) Role Overview: You are seeking a Data Scientist to support the development and deployment of machine learning models and analytics solutions aimed at enhancing decision-making throughout the mortgage lifecycle. In this hands-on role, you will contribute to building predictive models, customer segmentation tools, and automation workflows to improve operational efficiency and customer outcomes. Your work will involve collaborating closely with senior data scientists and cross-functional teams to translate business requirements into well-defined modeling tasks, with opportunities to utilize natural language processing (NLP), statistical modeling, and experimentation frameworks in a regulated financial setting. You will report to a senior leader in Data Science. Key Responsibilities: - Develop and maintain machine learning models and statistical tools for various use cases like risk scoring, churn prediction, segmentation, and document classification. - Collaborate with Product, Engineering, and Analytics teams to identify data-driven opportunities and support automation initiatives. - Translate business questions into modeling tasks, contribute to experiment design, and define success metrics. - Assist in constructing and managing data pipelines and model deployment workflows in collaboration with data engineering. - Apply techniques such as supervised learning, clustering, and basic NLP to structured and semi-structured mortgage data. - Support model monitoring, performance tracking, and documentation to ensure compliance and audit readiness. - Contribute to internal best practices, participate in peer reviews, and engage in knowledge-sharing sessions. - Stay updated with advancements in machine learning and analytics relevant to mortgage and financial services. Qualifications: - Minimum education required: Masters or PhD in engineering, math, statistics, economics, or related fields. - Minimum years of experience required: 2 (or 1 post-PhD), preferably in mortgage, fintech, or financial services. - Required certifications: None - Specific skill or ability needed: Experi