{"id":3566289,"url":"https://alion.io/job/exl-machine-learning-modeling-lead-credit-modeling","title":"Machine Learning Modeling Lead - Credit Modeling","company":{"id":38016,"name":"EXL","domain":"exlservice.com","url":"https://alion.io/company/exl","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"B","score":75,"open_postings":232,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":27,"computed_at":"2026-11-01T06:10:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":162000,"max_usd":336000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":782},"experience_years_min":9,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Docker","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-10-27T16:16:34Z","employer_posted_date":"2026-10-27","last_verified_at":"2026-11-01T12:49:02Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"We are seeking a highly skilled and experienced Machine Learning Modeling Lead - Credit Modeling to join our ML Model Innovation team within the banking/fintech domain (digital lending). The ideal candidate will be responsible for leading the development and deployment of machine learning models that power key business decisions such as collections models, credit risk scoring, fraud detection, customer segmentation, and personalized financial services.\nThe Individual needs to have strong knowledge of banking business, data and domain across the customer lifecycle as well as bureau data. They will collaborate with cross-functional teams and provide technical leadership to junior ML modelers and data scientists.\n Key Roles and Responsibilities -\nLead end-to-end ML solution development & innovation from data exploration, feature engineering, model development, validation, deployment, and monitoring. \nDevelop robust models which can drive business benefits. Support and review junior scientist submissions and share enhancement suggestions\nResponsible for documentation/documentation reviews, model reviews and submission \nResponsible for managing queries raised by Validation teams for the model\nCollaborate with implementation teams to deploy models into production environments (cloud or on premises).\nWork closely with business stakeholders to translate banking domain challenges into data-driven solutions.\nGuide junior data scientists and engineers on best practices in model development\nContinuously evaluate new tools, technologies, and frameworks relevant to ML in finance.\nPublish internal research and promote a culture of innovation and experimentation.\n Candidate Profile:\nStrong business knowledge of banking analytics across the retail banking customer lifecycle.\n9+ years of experience in applied machine learning model development in the banking or financial services domain.\nHands-on experience leading ML projects and teams.\nStrong experience with model development, deployment and monitoring in production environments.\nFamiliarity with collections, underwriting, fraud and ethical considerations in banking ML models.\nDemonstrable leadership ability, superior problem solving and people management skills\nMaster’s or Similar in Computer Science, Data Science, Statistics, Applied Mathematics, or a related quantitative field\nSkills:\nExpert in Python, SQL, ML libraries (Numpy, Pandas, Scikit-learn, TensorFlow, PyTorch) and techniques (Regression, Decision Trees, Ensembles: XGBoost, GBM, Random Forest, Unsupervised Learning, etc.).\nKnowledge of MLOps frameworks (MLflow, Kubeflow, Airflow, Docker, Kubernetes) is added benefit.\nStrong grasp of statistical modeling, optimization, and deep learning techniques.\nExcellent communication skills and ability to explain complex concepts to non-technical 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