Minimum work experience: 2+ years of advance analytics. Data science experience mandatory.

Preferred experience: 1+ years in Financial Crimes Compliance model validation

Responsibilities

- Support functional SME teams to build data driven Financial Crimes solution
- Conduct statistical testing of the screening matching algorithms, risk rating models and thresholds configured for detection rules
- Validate data models of fraud / financial crime systems built on systems such as SAS Viya, Actimize, Lexis Nexis, Napier, etc.
- Develop, validate, and maintain fraud models to detect suspicious activities, transactions, customer risk, money mules, etc.
- Conduct thorough model validation processes, including performance monitoring, tuning, and calibration.
- Ensure compliance with regulatory requirements and internal policies related to AML model risk management.
- Collaborate with cross-functional teams to gather and analyze data for model development and validation.
- Perform data analysis and statistical modeling to identify trends and patterns in financial transactions.
- Prepare detailed documentation and reports on model validation findings and recommendations.
- Assist in feature engineering for improvising Gen AI prompts applicable for automation of AML / Screening related investigations
- Use advanced Machine Learning deployment (e.g. XGBoost) and GenAI approaches

Criteria:





- Bachelors degree from accredited university
- 2+ years of complete hands-on experience in Python with an experience in Java, Rapid, Django, Tornado or Flask frameworks
- Working experience in Relational and NoSQL databases like Oracle, MS SQL MongoDB or ElasticSearch
- Proficiency BI tools such as Power BI, Tableau, etc.
- Proven experience in data model development and testing
- Education background in Data Science and Statistics
- Strong proficiency in programming languages such as Python, R, and SQL.
- Expertise in machine learning algorithms, statistical analysis, and data visualization tools.
- Familiarity with regulatory guidelines and standards for AML
- Experience in fraud related model validation and testing
- Expertise in techniques and algorithms to include sampling, optimization, logistic regression, cluster analysis, Neural Networks, Decision Trees, supervised and unsupervised machine learning

Preferred experiences:

- Validation of financial crimes compliance models such as statistical testing of customer / transaction risk models, screening algorithm testing, etc.
- Experience with developing proposals (especially new solutions)
- Experience working FCC technology platforms e.g. Norkom, SAS, Lexis Nexis, etc.
- Hands on experience with data analytics tools using Informatica, Kafka, etc.

📌 FCC - Intelligence / Consulting (Gurugram)
🏢 KPMG Assurance and Consulting Services
📍 Gurugram