MUST: Strong data science exp, SQL coding, hands on experience and ML/LLM models

Responsibilities

• As part of the Global Reconciliation Utility (GRU) and Data Engineering team, this role will be responsible for development and implementation of GRU ML models strategies.
• Lead ideation, technical development, and launch of innovative AI ML models and ensure the design and usage adheres to the overall Citi's architecture blueprint.
• Undertake preprocessing of structured and unstructured data
• Analyze substantial amounts of information to discover trends and patterns.
• Build predictive models and machine-learning algorithms.
• Combine models through ensemble modeling.
• Present information using data visualization techniques.
• Propose solutions and strategies to business challenges.
• Collaborate with engineering and operational teams to ensure ML Models success and integration.
• Provide in-depth analysis with interpretive thinking to define issues and develop innovative solutions.
• Define ML Models roadmaps and schedules in collaboration with technical and business teams.
• Facilitate the creation and maintenance of proper ML Models documentation.
• Foster strong relationships with key stakeholders and external partners to support ML Models development.

Qualifications

• AI/ML Development and Big Data experience
• Expertise with data reconciliations, data quality, large data processing and controls
• Expertise leading large scale Enterprise Transformation initiatives with a clear track record of success.
• Ability to articulate intricacies of Technology Architecture
• Deep functional and technical experience in Capital Markets across Front Office, Middle Office, Operations, Risk & Finance.
• Demonstrated leadership, management, and development skills.
• Experience with predictive analytics using large and complex datasets.
• Enthusiastic about AI and ML optimizations and open-source technologies

Skills Required

• Strong problem-solving skills with an emphasis on ML Models development.
• Experience working with and creating data architectures.
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, and proper usage, etc.) and experience with applications.