Data Scientist

As a Data Scientist specializing in Financial Crime use cases, your role involves developing and deploying AI/ML models for various purposes such as AML, fraud detection, scam detection, and risk intelligence. You will work with large-scale structured and unstructured datasets using Python, SQL, and PySpark to build scalable data and ML pipelines. Your responsibilities also include building and optimizing cloud-native AI solutions and workflows on AWS, applying statistical analysis, feature engineering, experimentation, and model evaluation techniques to enhance model performance. Key Responsibilities: - Develop and deploy AI/ML models for Financial Crime use cases including AML, fraud, scam detection, and risk intelligence - Work with large-scale structured and unstructured datasets using Python, SQL, and PySpark to build scalable data and ML pipelines - Build and optimize cloud-native AI solutions and workflows on AWS - Apply statistical analysis, feature engineering, experimentation, and model evaluation techniques to improve model performance - Collaborate with engineers, product owners, and domain experts to solve complex business problems and deliver impactful AI solutions - Support model deployment, monitoring, and continuous improvement of production ML systems - Contribute to best practices across responsible AI, model governance, and ML Ops Qualifications Required: - 5+ years of experience in Data Science, Machine Learning, or Applied AI - Strong hands-on experience with Python, PySpark, SQL, and AWS - Experience working with large-scale datasets and distributed data processing frameworks - Experience building, evaluating, and deploying machine learning models in production environments - Strong analytical, problem-solving, and stakeholder communication skills - Exposure to Financial Crime, Fraud, AML, Risk, or Banking domains is highly regarded - Experience in building and shipping Generative and Agentic AI-based systems Please note that there are no additional details about the company provided in the job description. As a Data Scientist specializing in Financial Crime use cases, your role involves developing and deploying AI/ML models for various purposes such as AML, fraud detection, scam detection, and risk intelligence. You will work with large-scale structured and unstructured datasets using Python, SQL, and PySpark to build scalable data and ML pipelines. Your responsibilities also include building and optimizing cloud-native AI solutions and workflows on AWS, applying statistical analysis, feature engineering, experimentation, and model evaluation techniques to enhance model performance. Key Responsibilities: - Develop and deploy AI/ML models for Financial Crime use cases including AML, fraud, scam detection, and risk intelligence - Work with large-scale structured and unstructured datasets using Python, SQL, and PySpark to build scalable data and ML pipelines - Build and optimize cloud-native AI solutions and workflows on AWS - Apply statistical analysis, feature engineering, experimentation, and model evaluation techniques to improve model performance - Collaborate with engineers, product owners, and domain experts to solve complex business problems and deliver impactful AI solutions - Support model deployment, monitoring, and continuous improvement of production ML systems - Contribute to best practices across responsible AI, model governance, and ML Ops Qualifications Required: - 5+ years of experience in Data Science, Machine Learning, or Applied AI - Strong hands-on experience with Python, PySpark, SQL, and AWS - Experience working with large-scale datasets and distributed data processing frameworks - Experience building, evaluating, and deploying machine learning models in production environments - Strong analytical, problem-solving, and stakeholder communication skills - Exposure to Financial Crime, Fraud, AML, Risk, or Banking domains is highly regarded - Experience in building and shipping Generative and Agentic AI-based systems Please note that there are no additional details about the company provided in the job description.