AI Data Team Lead

The AI Data Team Lead is responsible for leading a team of data analysts and BI specialists to ensure the data foundation, reporting, and monitoring of AI solutions are world-class. This role focuses on the analytical lifecycle of AI: preparing high-quality datasets, monitoring model performance in production, and driving business value through advanced BI and "AI Data Agents."

The Lead acts as a critical bridge between business stakeholders, internal IT/DWH teams, and external AI partners, ensuring that data science outputs are measurable, governed, and integrated effectively into banking operations.

Key Responsibilities:

Leadership and BI Center of Excellence

• Lead and mentor a team of analysts and BI specialists focused on AI support and data delivery.

• Manage a BI Center of Excellence, setting standards for Power BI development, data visualization, and automated reporting.

• Drive the creation and maintenance of Datamarts specifically optimized for business insight and performance tracking.

• Oversee the development and support of AI Data Agents (e.g., LLM-based tools for data retrieval and automated insights).

AI Monitoring and Ad-hoc Analytics:

• Perform ad-hoc analytics to evaluate the effectiveness of deployed AI solutions and identify areas for operational improvement.

• Design and maintain regular reporting and monitoring frameworks to track AI model health, accuracy, and business KPIs (e.g., CSAT, automation rates).

• Conduct deep-dive root cause analyses when AI performance deviates from expected benchmarks.

Data Engineering and AI Preparation:

• Manage the preparation and labeling of datasets required by external partners or internal teams for ML model training.

• Collaborate closely with IT and DWH teams to ensure robust data pipelines and the availability of high-quality transactional data.

• Support the technical deployment and integration of ML models into production environments.

Strategic Cooperation and Governance:

• Serve as the primary liaison for external AI vendors and internal data science teams, acting as the "owner" of the data inputs and analytical outputs.

• Contribute to Data Governance within the AI domain, ensuring data privacy, security, and documentation standards are strictly followed.

• Translate business requirements from contact center or retail banking leaders into technical data specifications.

Required Skills and Qualifications

Leadership and Management:

• Proven experience leading an analytical or BI-focused team, with a track record of delivering complex data projects.

• Strong stakeholder management skills, with the ability to coordinate between business units and technical IT/DWH teams.

Data Analytics and BI Expertise:

• Expert-level Power BI: Experience in building enterprise-grade dashboards, DAX, and managing Power BI workspaces.

• Data Modeling: Strong understanding of Star/Snowflake schemas and Datamart design.

• Analytical Mindset: Ability to perform complex ad-hoc analysis to validate AI performance and business impact.

Technical Proficiency:

• SQL Mastery: Expert skills in querying large-scale data platforms (e.g., Oracle DWH, Impala, Hadoop).

• Data Manipulation: Proficiency in Python (specifically Pandas/NumPy) for data preparation and ad-hoc scripting.

• AI Tooling: Familiarity with the AI/ML Lifecycle (CRISP-DM or similar) to support deployment and monitoring without performing the actual modeling.

• Big Data: Experience with PySpark or Spark SQL for handling large transactional datasets.

Strategic Thinking:

• Ability to contribute to Data Governance frameworks (metadata management, data quality, and lineage).

• Understanding of how "AI Data Agents" and RAG (Retrieval-Augmented Generation) architectures utilize corporate data.

Tools and Technologies:

• BI & Visualization: Power BI (Desktop/Service), Tableau, or similar.

• Data Platforms: Oracle DWH, Hadoop/Impala, SQL.

• Programming & Data Prep: Python (Pandas, Streamlit for Data Agents), PySpark.

• Productivity & Collaboration: Git, JIRA, Confluence.

• AI Operations: Familiarity with ML monitoring tools and API integrations.