Responsibilities
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Information delivery & analytics. State-of-the-art expertise across, data/information preparation, data insight & visualization using BI (or similar tools), and advanced data prediction using AI, ML, DL, etc. AI/ML Ops. Responsible for integration, deployment and monitoring of AI/ML products and solutions, Data management. Demonstrate expertise in data management to ensure the analytics products are appropriate/ethical and well-controlled.
Enabling data architecture and delivery of data-analytics platforms and Solutions- on-premises, cloud, and hybrid ensuring adherence and conformance to organization standards and policies Be a trusted partner. Shape the information & analytics agenda at organization, and work with all of Organization’s businesses in laying out their information & analytics adoption roadmaps. Risk Mindset: Familiar with risk and controls frameworks and ability to operate with a control mindset Skills, experience, qualifications and knowledge required:
Core Skills requirement:
Designing and developing scalable data pipelines to collect and process large volumes of data from multiple sources. Building physical data models and ETL processes to ensure data quality, integrity, and accessibility. Microservices Development: Building and maintaining highly scalable and fault tolerant microservice, including efficient server-side APIs. Deployment: Hands on with CI/CD, Jenkins, Ansible, DevOps process, Enterprise integration patterns. Hands-on with programming languages (Python, SQL, Java, Unix scripting etc.) and with orchestration tools like Airflow or Autosys Experience with cloud technologies such as EC2, EMR, Snowflake or similar tools with ability to drive design and data model discussions, hybrid data architecture. Proficiency in React with hands-on experience in UI development a plus. Ability to understand and integrate cultural differences and work effectively with virtual cross-cultural, cross-border teams. Flexibility to adjust to multiple demands, shifting priorities, ambiguity, and rapid change. Experience with senior stakeholder management will be an added advantage. Excellentmunication (verbal, written, listening), presentation, and interpersonal skills. Able to analyzeplex situations and derive workable actions. Able to constructively challenge requirements and current state to increase overall value to the firm. Education and experience
Wide variety of degrees will be considered, however work experience will be of equal, if not greater importance
At least 4-year Bachelor’s degree in quantitative fields with minimum of 5 years of relevant data experience in data engineering / MLOps, full stack engineering, preferably in financial organizations or Masters in quantitative fields puter Science, Statistics or similar) Experience of working with a multi-cultural, multi-disciplined, globally dispersed teams Certifications in relevant technologies or frameworks are a plus.