Position Description:
Designs, develops, and implements end-to-end (E2E) Machine Learning (ML) solutions on Cloud and traditional infrastructure technologies. Provides expertise during the expansion of Artificial Intelligence/ Machine Learning (AI/ML) transformation initiatives and ongoing Cloud migrations. Generates visualization dashboards using Tableau or Angular. Builds Continuous Integration and Continuous Deployment (CI/CD) pipelines to deploy code into production. Drives development and implementation efforts to support expanded automated solution discovery and detection efforts.
Primary Responsibilities:
Provides ML solutions for data-driven and actionable insights to enable objective informed decisions. Maintains technical infrastructure to remain compliant with updates. Collaborates with vendors to evaluate machine learning models. Tests technical solutions in the production environment to ensure optimal function. Develops new solutions in cloud-native scalable architecture. Possesses a unique blend of academic and domain specific knowledge in data science, AI/ML technologies, and application development. Communicates revenue or cost saving benefits to senior leadership.Education and Experience:
Bachelor’s degree (or foreign education equivalent) in Computer Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and five (5) years of experience as a Principal Data Modeler (or closely related occupation) building data science applications from inception to production at scale using Python, advanced analytics, statistics, and ML and Deep Learning (DL) techniques.
Or, alternatively, Master’s degree (or foreign education equivalent) in Computer Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and three (3) years of experience as a Principal Data Modeler (or closely related occupation) building data science applications from inception to production at scale using Python, advanced analytics, statistics, and ML and Deep Learning (DL) techniques.
Skills and Knowledge:
Candidate must also possess:
Demonstrated Expertise (“DE”) coordinating with business, product, and engineering stakeholder teams to analyze institutional clients’ business requirements — converting requirements to ML/AI solutions and assessing the impact of ML/AI solutions using experimentation design. DE driving analytics and ML initiatives – collaborating in the implementation and deployment of major advanced analytics and ML based projects within a financial services environment; and owning and implementing advanced analytics and ML based projects –ideation, implementation, and providing guidance, training, and thought leadership — within a financial services environment. DE designing and developing scalable and secure in-house solutions using Cloud technologies (EC2, AWS Sagemaker, Amazon Elastic Kubernetes Service, S3, and Jenkins pipelines) according to standard security practices, distributed architectural requirements, and fine-grain access controls. DE performing data collation using SQL; performing data manipulation, exploration, analysis, visualization, and reporting using Python; driving purchasing decisions by testing and evaluating data science vendor products using Python; and presenting findings to management using Python, Tableau, and PowerPoint presentations.#PE1M2
Certifications: