Job Description
Role Overview :
We are seeking aGraph Database SMEto architect and implement advancedgraph-based data modelsand analytics capabilities within a large banking data platform. The candidate will be responsible for designing scalable graph solutions to uncover hidden relationships, improve customer intelligence, detect fraud networks, and enhance compliance visibility.
Key Responsibilities: Design and implement
property graph or RDF-based data models to capture complex entity relationships (e.g., customeraccountaddressdevicetransaction). Work with data architects to translate relational data models to
graph structures suitable for graph traversal and inference. Deploy, configure, and optimize
graph database platforms such as
Neo4j , DGraph Ingest data from Cloudera/HDFS, RDBMS, or real-time Kafka streams into the graph store. Develop
complex graph queries using
Cypher , Gremlin , or SPARQL for relationship discovery, pattern matching, and path analysis. Build use cases for
CIF resolution , fraud rings , KYC hierarchies , risk propagation , , influence networks . Expose graph APIs or integrate with downstream applications and BI/ML platforms for analytics consumption. Required education
Bachelor's Degree Preferred education
Master's Degree Required technical and professional expertise
Experience 6+ years
Graph Database Fundamentals Understanding graph structures, nodes, edges, and relationships.
Query Languages Familiarity with SPARQL (for RDF-based GraphDB) or Cypher (for Neo4j).
Database Management Experience with database setup, indexing, and optimization.
Programming Skills Proficiency in languages like Python, Java, or JavaScript for database interaction.
Data Modeling Ability to design efficient graph schemas and relationships.
Job Classification
Industry: IT Services & Consulting
Functional Area / Department: Data Science & Analytics
Role Category: Data Science & Machine Learning
Role: Full Stack Data Scientist
Employement Type: Full time
Contact Details:
Company: IBM
Location(s): Mumbai
Keyskills:
python
javascript
java
neo4j
data modeling
rdbms
natural language processing
bi
machine learning
artificial intelligence
sql
database management
deep learning
r
data science
spark
predictive modeling
kafka
text mining
hadoop
sparql
graph databases