Job Description
Job Description POSITION TITLE: Director, Data Science and Machine Learning Position Summary: We're looking for a data science leader to lead high-impact machine learning initiatives that drive business outcomes across the mortgage lifecycle-from acquisition to servicing. In this hands-on role, you'll design and deploy machine learning models, advanced analytics, and experimentation frameworks that power data-driven decisioning at scale. You'll translate complex business problems into scalable, production-ready data science solutions, with a focus on predictive modeling, customer segmentation, conversion optimization, and automation of decision workflows in the mortgage domain. This role reports to a senior leader in Data Science and is highly visible to top leadership, and across analytics, finance, and product teams. Job Functions and Responsibilities: To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. • Design and implement machine learning models and statistical techniques across key mortgage workflows-such as risk scoring, churn prediction, segmentation, and structured document processing where applicable-to improve metrics like Conversion Rate, Delinquency Rate, and Customer Lifetime Value (CLV). • Work closely with cross-functional partners (Product, Marketing, Engineering, and Finance) to identify opportunities for automation and insight generation using advanced analytics. • Translate business problems across the mortgage lifecycle (e.g., underwriting, servicing, collections) into well-scoped modeling initiatives. • Drive the development of automation-enhanced decision systems (e.g., pre-fill models for underwriting, early delinquency risk alerts, servicing escalation predictors) to enhance operational efficiency and user experience. • Build robust data pipelines and modeling systems in collaboration with data engineering, ensuring scalability, monitoring, and model governance in production environments. • Apply state-of-the-art techniques in self-supervised learning, graph-based modeling, and Bayesian mixture models to extract value from complex behavioral and relational data (e.g., referral networks, shared IP patterns). • Contribute to a culture of excellence by publishing internal best practices, conducting peer reviews, and mentoring early-career data scientists informally (with no requirement to directly manage people). • Actively contribute to the broader data science and machine learning community through publications, conference presentations, open-source contributions, or internal/external thought leadership, helping establish the company as a leader in applied AI for mortgage and financial services. • Apply natural language processing (NLP) techniques as needed for tasks such as structured document parsing, entity extraction from disclosures, or classification of customer inquiries. • Ensure compliance with data privacy regulations (e.g., GDPR, CCPA) and model auditability, particularly in financial and regulatory-sensitive applications. Qualifications: To perform this job successfully, an individual must have the following education and/or experience: • Minimum education required: Masters or PhD in engineering/math/statistics/economics, or a related field • Minimum years of experience required: 6 (or 3, post-PhD), ideally in mortgage, fintech, or financial services • Required certifications: None • Specific skill or ability needed • Strong analytical and modeling skills, with experience applying machine learning to structured, unstructured, and semi-structured mortgage or financial data. • Demonstrated ability to translate business objectives into technical modeling goals and measurable success metrics. • Experience building models for fraud detection, CLV estimation, or risk scoring is required; experience with NLP for structured document classification or disclosure review, compliance automation or underwriting is a plus. • Familiarity with modern ML Ops practices (data/model versioning, CI/CD, performance monitoring, and bias detection). • Experience with regulatory compliance, privacy, and auditability in model development (e.g., ECOA, RESPA, CFPB regulations). • Excellent communication skills for both technical and non-technical stakeholders, including ability to present complex results with clarity. • Minimum software or applications experience required/preferred • Advanced proficiency with Python (including scikit-learn, PyTorch, TensorFlow, Keras), SQL, and distributed computing tools (e.g., Spark, Hadoop). • Minimum experience required using mobile technology: None • Any other requirements an ideal applicant needs to have that is not covered by above: None Training / Licensing Requirements: • Must pass the Company's Background Screening process prior to beginning employment. Additionally, as a condition of employment, you may be required to pass client-specific background check requirements or Federal/State licensing requ
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