The Associate, Risk Modeling conducts robust validations of a wide variety of risk models against established standards, develops benchmarks, and challenges and replicates models where applicable. Manages models of advanced complexity and leads special projects and/or programs.
Reviews and assesses overall model health within a given framework, identifies potential problems and works with stakeholders to resolve.
Conducts robust validations of a wide variety of models against established standards, develops benchmarks, challenges, and replicates models where applicable.
Assesses model performance and evaluates model assumptions and weaknesses.
Collaborates with model owners and develops to understand the business context for models.
Ensures adherence to the policies and procedures established by the company.
Qualifications: To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Education: Bachelor's Degree or equivalent work experience. Master’s or equivalent degree in Statistics, Mathematics, Engineering, Computer Science, Economics, or related fields is a preferred.
Work Experience: 5+ years developing or validating quantitative machine learning (ML) models or Large Language Models (LLMs) frameworks. Experience in the financial services industry, including expertise in risk modeling and regulatory compliance (SR 11-7) is preferred.
AI/ML Expertise: Demonstrated experience with Artificial Intelligence and Machine Learning (AI/ML) frameworks such as TensorFlow, PyTorch and Scikit-learn. Hands-on experience with gradient boosting models, NN and other ML algorithms for classification and regression.
LLM Knowledge: Experience with LLMs framework including model’s architecture, methodology, implementation, APIs, and evaluation, with working knowledge of prompt-tuning and RAG/fine-tuning LLMs applications. Leaderboards review. Experience with Hugging Face models and datasets, and testing design and execution for GenAI risks such as hallucination, accuracy, toxicity, prompt injection, bias etc.
Programming skills: Proficiency in Python and R. Experience in cloud-based solutions (AWS, Azure) is a plus.
Excellent organizational skills, strong communication, with the ability to work cross-functionally with model developers, data engineers, technology, and governance teams.
EEO Statement: At Santander, we value and respect differences in our workforce. We actively encourage everyone to apply.
Santander is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, genetics, disability, age, veteran status or any other characteristic protected by law.
This job description does not list all of the job duties of the job. You may be asked by your supervisors or managers to perform other duties. You may be evaluated in part based upon your performance of the tasks listed in this job description. The employer has the right to revise this job description at any time. This job description is not a contract for employment and either you or the employer may terminate at any time for any reason.
Primary Location: Boston, MA, Boston
Other Locations: Massachusetts-Boston,Texas-Dallas,New York-New York
Organization: Santander Holdings USA, Inc.
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