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As a Data Analyst, you will play a critical role in driving informed decision-making processes by analyzing complex data sets and transforming raw data into actionable insights. The ideal candidate should have worked in the banking domain and possess a strong analytical mindset, exceptional problem-solving capabilities, and a solid understanding of financial metrics. The candidate should have data migration experience. Your responsibilities will include developing and maintaining dashboards, conducting in-depth market research, performing trend analysis, and collaborating with cross-functional teams to ensure data integrity. This role requires strong proficiency in data visualization tools, statistical analysis software, and the ability to communicate findings effectively to stakeholders. Additionally, the role increasingly involves leveraging modern AI/ML techniques to enhance data insights and automation.
Job Responsibility
Lead data migration activities and analysis
Work with Data scientists/Business Analysts/Product Owner to understand MI and dashboard requirements
Design Data Solutions: Leverage your analytical skills to design innovative data solutions that address complex business requirements and drive decision-making
Create detailed field level mappings as needed for different MI Dashboards
Develop and maintain comprehensive dashboards to visualize key performance indicators
Conduct market research and trend analysis to inform strategic business decisions
Collaborate with cross-functional teams to ensure data accuracy and integrity
Identify and recommend process improvements based on data analysis findings
Prepare detailed reports and presentations on analytical findings for stakeholders
Stay updated on industry trends, data analysis techniques, and best practices
Requirements
Minimum of 8+ years of experience in data engineering or a similar role
Strong Python skills for data analysis and software development, with familiarity in libraries/frameworks such as PyTorch or TensorFlow
Hands-on experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and prompt engineering
Understanding of classical ML algorithms, data preprocessing techniques, and model evaluation methods
Experience with agentic and orchestration frameworks such as LangChain, AutoGen, or CrewAI
Experience deploying data/ML solutions on cloud platforms (AWS, Azure, GCP) and working with MLOps tools such as Docker