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.