01Responsibilities
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily.
Develop and maintain machine learning models and statistical tools for use cases such as 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 and contribute to the design of experiments and success metrics.
Assist in building and maintaining data pipelines and model deployment workflows in partnership 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 and participate in peer reviews and knowledge-sharing sessions.
Stay current with developments in machine learning and analytics relevant to mortgage and financial services.
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: 2 (or 1, post-PhD), ideally in mortgage, fintech, or financial services
Required certifications: None
Specific skill or ability needed
Minimum software or applications experience required/preferred
Minimum experience required using mobile technology: None
Any other requirements an ideal applicant needs to have that is not covered by above: 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.
Work Schedule:
2pm to 11pm IST .
