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Mid-level data scientist, broad skills and popular role title attract many qualified applicants.
Credit/card model risk and regulatory focus make skills less transferable across unrelated industries.
Explicit 2-3 year requirement, mandated tech stack and regulated model-risk controls increase filtering strictness.
Develop and tune predictive models using machine learning techniques on large datasets under supervision.
Translate analytical results into actionable business recommendations and support solution implementation.
Ensure compliance with model risk management policies and contribute to regulatory governance documentation.
Bachelor’s degree in Statistics, Mathematics, Engineering, Data Science, Economics, Computer Science, or related quantitative field.
2-3 years of relevant work experience in data science or advanced analytics.
Proficiency in Spark SQL, Python, SAS/STAT, SQL, machine learning, and cloud platforms like AWS.
Must handle data extraction, processing large datasets, and model validation tasks as part of responsibilities.
Experienced in developing and deploying statistical and machine learning models in a financial services environment.
Able to communicate complex model results and business impact clearly with internal stakeholders.
Familiar with model risk management practices and regulatory requirements in credit or banking sectors.