Data Analyst

About the role:

The Data Analyst's duties include developing, executing, and refining service requests, which may involve routine monitoring of online platforms for potential security and operational hazards, escalation procedures, and data report creation. This role involves direct interaction with clients and SIS management and requires providing specialized guidance on security intelligence matters. The ideal candidate possesses a robust foundation in compliance and data analytics, with proficiency in SQL, Python, Tableau, and other relevant tools. This position reports to the Senior Retail Operations Manager

In this role, your responsibilities will be:

• Investigative data analysis, pulling and reconciling data across multiple systems

• Use statistical and machine-learning techniques to build scalable systems.

• Maintain and update live data pipelines and operationalize new data pipelines as additional data sources are identified within the scope of deliverables.

• Analyze and extract relevant information from large amounts of the Client’s historical business data to help automate and optimize key processes.

• Design, development, and evaluation of highly innovative models for predictive learning.

• Establish scalable, efficient, automated processes for large-scale data analyses, model development, validation, and implementation.

• Building SOPs for gathering data and searching specific systems

• Regular control monitoring report resolutions

• Use individual problem-solving and analytical skills to authenticate customers

• Perform other related duties as required

• Design, build, and deploy AI-based analytical workflows (including LLM-powered tools and agentic pipelines) to automate security intelligence monitoring, anomaly detection, and reporting tasks.

• Develop, test, and refine prompts for AI/LLM applications; fine-tune models on domain-specific data to improve accuracy, relevance, and consistency of outputs for security and compliance use cases.

• Establish evaluation frameworks and QC processes (accuracy benchmarks, hallucination checks, bias/drift monitoring) to continuously assess and improve AI model and pipeline performance.

• Apply understanding of modern software development practices — version control, code review, testing — to build maintainable, production-grade data and AI pipelines; CI/CD pipeline design and deployment automation.

• Support infrastructure management for data/AI systems (cloud resources, containerization, environment configuration) and contribute to CI/CD automation for reliable, scalable deployment of models and data pipelines.