Apps Dev Tech Lead Analyst - Vice President

The Applications Development Technology Lead Analyst is a senior level position responsible for establishing and implementing new or revised application systems and programs in coordination with the Technology team. The overall objective of this role is to lead applications systems analysis and programming activities.

• Partner with multiple management teams to ensure appropriate integration of functions to meet goals as well as identify and define necessary system enhancements to deploy new products and process improvements

• Resolve variety of high impact problems/projects through in-depth evaluation of complex business processes, system processes, and industry standards

• Provide expertise in area and advanced knowledge of applications programming and ensure application design adheres to the overall architecture blueprint

• Utilize advanced knowledge of system flow and develop standards for coding, testing, debugging, and implementation

• Develop comprehensive knowledge of how areas of business, such as architecture and infrastructure, integrate to accomplish business goals

• Provide in-depth analysis with interpretive thinking to define issues and develop innovative solutions

• Serve as advisor or coach to mid-level developers and analysts, allocating work as necessary

• Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency.

• Lead the design and execution of complex data analysis and AI/ML initiatives across large, structured, and unstructured datasets.

• Develop and deploy predictive, classification, clustering, and forecasting models to support business strategy and risk management.

• Build and maintain advanced dashboards, KPIs, and automated reporting frameworks to monitor business performance.

• Partner with business stakeholders to translate requirements into analytical and machine learning solutions.

• Design and implement feature engineering pipelines and model evaluation frameworks.

• Collaborate with Data Engineering teams to ensure scalable data pipelines and ML-ready datasets.

• Operationalize machine learning models through production deployment and monitoring (MLOps practices).

• Analyze trends, anomalies, and behavioral patterns using statistical and machine learning techniques.

• Ensure model governance, explainability, fairness, and compliance with regulatory requirements.

• Automate analytics workflows and implement scalable AI-driven solutions.

• Present analytical findings and model insights to senior leadership and cross-functional teams.

• Mentor junior analysts and data scientists on advanced analytics and ML best practices.

• Drive continuous improvement in analytical methodologies, model performance, and reporting standards.

• Influence strategic decisions through data science and AI-powered insights.

• Manage multiple priorities in a fast-paced, highly regulated environment.

• At least 6+years of relevant experience in Data Analytics, Data Science, or Advanced Analytics roles.

• Extensive experience system analysis and in programming of software applications

• Experience in managing and implementing successful projects

• Subject Matter Expert (SME) in at least one area of Applications Development

• Ability to adjust priorities quickly as circumstances dictate

• Demonstrated leadership and project management skills

• Consistently demonstrates clear and concise written and verbal communication

• Advanced proficiency in SQL and relational database concepts.

• Strong programming experience in Python (required);
  PySpark or R preferred.

• Hands‑on experience building and deploying machine learning models (supervised and unsupervised).

• Experience with ML libraries such as scikit‑learn, XGBoost, Tensor Flow, or PyTorch.

• Strong knowledge of statistical modeling, feature engineering, and model validation techniques.

• Experience with BI tools such as Tableau or…