Data Scientist (ML)

Job Description: As a Machine Learning Data Scientist with 4-12 years of experience in the Banking / Financial Services domain, you will play a crucial role in developing, validating, and deploying machine learning models to address complex business challenges across risk, fraud, customer analytics, lending, collections, and marketing functions. Your expertise in predictive modeling, statistical analysis, and banking analytics will be essential in driving impactful insights and recommendations to senior stakeholders. Key Responsibilities: - Design, develop, and deploy machine learning models tailored for banking use cases. - Analyze large structured and unstructured datasets to uncover business opportunities and risks. - Build predictive and classification models utilizing advanced ML algorithms. - Perform feature engineering, model tuning, validation, and performance monitoring. - Collaborate with various stakeholders including business, product, risk, and technology teams to translate business requirements into analytical solutions. - Develop comprehensive model documentation and ensure model governance compliance. - Present insights and recommendations effectively to senior stakeholders. - Engage in the end-to-end ML lifecycle encompassing data extraction, preprocessing, modeling, deployment, and monitoring. - Uphold compliance with banking regulations and model risk management standards. Qualifications: - Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, Economics, or related field. - 4-12 years of experience in Data Science, Machine Learning, or Advanced Analytics. - Proven experience in working within the Banking or Financial Services domain. - Possess strong statistical and analytical problem-solving skills to drive data-driven decisions. Job Description: As a Machine Learning Data Scientist with 4-12 years of experience in the Banking / Financial Services domain, you will play a crucial role in developing, validating, and deploying machine learning models to address complex business challenges across risk, fraud, customer analytics, lending, collections, and marketing functions. Your expertise in predictive modeling, statistical analysis, and banking analytics will be essential in driving impactful insights and recommendations to senior stakeholders. Key Responsibilities: - Design, develop, and deploy machine learning models tailored for banking use cases. - Analyze large structured and unstructured datasets to uncover business opportunities and risks. - Build predictive and classification models utilizing advanced ML algorithms. - Perform feature engineering, model tuning, validation, and performance monitoring. - Collaborate with various stakeholders including business, product, risk, and technology teams to translate business requirements into analytical solutions. - Develop comprehensive model documentation and ensure model governance compliance. - Present insights and recommendations effectively to senior stakeholders. - Engage in the end-to-end ML lifecycle encompassing data extraction, preprocessing, modeling, deployment, and monitoring. - Uphold compliance with banking regulations and model risk management standards. Qualifications: - Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, Economics, or related field. - 4-12 years of experience in Data Science, Machine Learning, or Advanced Analytics. - Proven experience in working within the Banking or Financial Services domain. - Possess strong statistical and analytical problem-solving skills to drive data-driven decisions.