Senior Data Scientist - Financial Crimes Data Hub (EFCC/FIU)

Role Purpose

Partner with EFCC, FIU, FCBDP, and Solytics stakeholders to design and validate a scalable Databricks-based analytical data model that supports:

- Financial Crimes investigations
- AML transaction monitoring
- Entity resolution
- Behavioral analytics
- Alert generation and triage
- Network/link analysis
- Solytics anomaly detection capabilities
- Future AI/ML use cases

The role will bridge business requirements, data architecture, and data science to establish a governed enterprise financial crimes data foundation.

Required Technical Skills

Databricks & Lakehouse Architecture

- Advanced Databricks experience
- Delta Lake design patterns
- Unity Catalog governance
- Medallion Architecture (Bronze / Silver / Gold)
- Data Product design
- Feature Store concepts
- Databricks SQL
- PySpark
- Spark optimization and performance tuning
- Data lineage and metadata management

Desired Experience:

- Designing analytical data models in Databricks
- Large-scale cloud migration initiatives
- Financial services lakehouse environments

Data Modeling Expertise

Conceptual Modeling

Ability to define:

- Party
- Customer
- Household
- Account
- Transaction
- Alert
- Case
- Investigation
- Watchlist
- Relationship
- Counterparty





entities and business relationships.

Logical Modeling

Experience designing:

- AML analytical models
- Fraud analytical models
- Network graph structures
- Feature-rich analytic marts
- Subject area models

Physical Modeling

Knowledge of:

- Delta tables
- Dimensional models
- Data Vault concepts
- Star and Snowflake schemas
- Slowly changing dimensions
- CDC processing
- Incremental loads

Financial Crimes Analytics

Solid understanding of:

AML

- KYC
- CDD
- EDD
- SAR workflows
- Customer risk scoring
- Alert dispositioning

Fraud

- Account fraud
- Payment fraud
- ACH fraud
- RTP fraud
- Behavioral anomalies

Investigations

- Entity resolution
- Link analysis
- Network analysis
- Case management workflows

Nice to have:

- Actimize experience
- Solytics experience
- Financial Services regulatory environment

Data Science & Machine Learning Skills

Statistical Modeling

- Supervised learning
- Unsupervised learning
- Time-series analysis
- Clustering
- Anomaly detection

Model Development

- Python
- PySpark
- Scikit-learn
- XGBoost
- MLflow
- Databricks Machine Learning

Explainability

- Feature importance
- Model governance
- Regulatory transparency
- Fairness and bias evaluation

📌 Data Scientistv + DataBricks (India)
🏢 GK HR Consulting India Private
📍 India