Data Analytics Analyst – Anti-Financial Crime

About the role

A reputed bank is looking for a Data Analyst to drive end-to-end analytics for Anti-Financial Crime projects. The role combines strong analytical and data science capabilities with clear storytelling, documentation and stakeholder leadership. You will work extensively on Hadoop Big Data, design analytic workflows, build ML models (including unsupervised techniques) and turn insights into business impact.

Key responsibilities

• Lead end-to-end analytics on Hadoop Big Data (Spark/Hive): data ingestion, exploratory data analysis, feature engineering, model development, validation, deployment and monitoring.
• Develop models for AML, fraud analytics, anomaly detection, customer segmentation and risk scoring using supervised and unsupervised ML.
• Create analytical workflows, automations and pipelines.
• Prepare documentation such as Functional Specification Documents, Model Design Documents, Business Impact Analysis and testing packs.
• Build dashboards and reports in Qlik Sense and present insights clearly.
• Lead stakeholder meetings, run workshops and deliver executive-ready presentations.
• Translate business requirements into analytical solutions and recommend data-driven strategies.
• Ensure compliance with model governance, data quality and security standards.
• Mentor junior analysts and data scientists.

Required profile

• Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Mathematics, Economics or a related field.
• 7+ years in Data Science / Data Analytics, including 4+ years in Banking / Financial Services.
• Experience building ETL pipelines and end-to-end analytical workflows.
• Strong understanding of machine learning, including unsupervised models (K-Means, DBSCAN, GMM, Isolation Forest, One-Class SVM, LOF).
• Excellent communication, consulting, slide creation and stakeholder storytelling skills.

Required skills

• Hive, Impala, Python (Pandas, NumPy, Scikit-learn), R, SQL and PySpark.
• Qlik Sense and Big Data tools (Hadoop, Spark, Hive/Impala).
• Preferred: ML framework experience, certifications in Data Science, Cloud Technologies or AML/Compliance, and experience in customer segmentation, anomaly detection and financial crime analytics.