What are the responsibilities and job description for the Data Scientist – Network Intelligence & Fraud Analytics position at Tredence Inc.?
Data Scientist – Network Intelligence & Fraud Analytics
Role Overview
We are hiring Data Scientists with strong telecom/network data expertise to build machine learning models for fraud detection using large-scale telecom signals.
Key Responsibilities
- Develop ML models for fraud detection using telecom financial datasets
- Engineer features from CDRs, network events, device fingerprints, geolocation data
- Build real-time and batch fraud scoring pipelines
- Implement anomaly detection, graph analytics, and behavioral modeling techniques
- Work with streaming platforms (Kafka) and large-scale data processing (Spark)
- Collaborate with SMEs to incorporate domain-driven features and rules
- Evaluate models using fraud-specific metrics (AUC, precision-recall, lift, cost savings)
- Optimize models for low latency and high scalability in production environments
Required Qualifications
- 5–10 years in Data Science / ML with strong telecom/network data experience
- Expertise in Python, ML frameworks (scikit-learn, PyTorch, etc.)
- Experience with big data tools (Spark, Hadoop) and streaming (Kafka)
- Strong understanding of network data structures (CDR, signaling, device data)
- Experience building fraud/anomaly detection models
Preferred
- Experience with graph-based fraud detection (network/relationship analysis)
- Exposure to real-time ML deployment (MLOps, APIs, Kubernetes)
- Background in both telecom and financial datasets (highly desirable)