Back
Tier-1 brand, metro location, and mid-level analytics role increase applicant competition.
Strong AML and banking focus requires domain experience, limiting cross-industry transferability.
Explicit 2–4 years, AML/domain experience, master's requirement and mandatory Python/SQL/Hive raise strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead development and performance review of Logistic Regression models and clustering for customer behavior analysis and threshold tuning in AML contexts.
Identify anomalies and outliers in transaction and customer data, support threshold tuning and segmentation projects.
Validate data quality, automate data preprocessing, and translate complex analytics into clear documentation and presentations for diverse business stakeholders.
2-4 years experience in Financial Services or Analytics industry.
Master's degree in a numerate subject (Mathematics, Operational Research, Business Administration, Economics, etc.) from a premier institute or equivalent demonstrated ability.
Strong skills in Python, SQL, Hive; experience with threshold tuning, Logistic Regression modeling, anomaly detection, clustering.
Experience in banking/finance domain, preferably with AML or Financial Crime Risk context; excellent written and verbal communication skills.
Experienced in applied statistical modeling and data analytics within financial services, especially AML or transaction monitoring.
Able to manage multiple quantitative projects including model development, validation, and threshold optimization with business impact focus.
Comfortable handling cross-geography collaboration and translating technical analysis into clear insights for non-technical audiences.