IND_Senior Security Engineer

Description

Role

Security Data Scientist

Required Technical Skill Set

Cyber Security Data Science, Security Knowledge Graphs (Graph Databases, Ontologies & Link Analysis), Data Engineering (BigQuery, Spark, GCP / Azure Data Pipelines), Machine Learning & Anomaly Detection, Attack Path Analysis, Python (Pandas, PyTorch, Scikit-Learn, NetworkX), Graph Query Languages (Cypher / Gremlin / SPARQL), Security Telemetry & SIEM/SOC Analytics

Experience Range

7 – 12 Years overall

Location

Hyderabad

technical/data science experience (with 3+ years applying machine learning, graph analytics, or data engineering to cyber security/fraud domains)

Must-Have (Candidates must demonstrate depth in core areas and a blend/combination of the following specialisms)

Cyber Security Data Science & ML

Proven experience designing, training, and deploying machine learning models specifically for security use cases (e.g., UEBA, insider threat detection, anomalous authentication patterns, network intrusion, automated threat scoring).

Security Knowledge Graphs & Graph Analytics

Hands-on experience modeling and querying complex security relationships (identities, assets, permissions, attack paths, vulnerabilities) using Graph Databases (Neo4j, Amazon Neptune, Azure Cosmos DB / Gremlin, AWS Neptune, or Memgraph) and graph traversal languages (Cypher, Gremlin, SPARQL).

Graph Machine Learning & Link Analysis

Practical knowledge of graph algorithms (centrality, community detection, shortest path, PageRank) and Graph Neural Networks (GNNs) or link prediction to uncover hidden relationships and blast radiuses across enterprise security assets.

Data Engineering & Big Data Pipelines

Strong data engineering expertise in designing scalable ETL/ELT pipelines to ingest, normalize, and enrich massive volumes of multi-cloud security telemetry and logs using GCP BigQuery / Dataproc, Azure Synapse / Databricks, Apache Spark, or Kafka.

Programming & Data Science Toolkit

Advanced proficiency in Python and standard data science libraries (Pandas, NumPy, Scikit-Learn, PyTorch / TensorFlow, NetworkX, Pyvis, DGL/PyG).

Security Domain Knowledge

Solid understanding of cyber security frameworks, threat intelligence feeds (MITRE Telecommunication&CK, STIX/TAXII), identity access graphs, and cloud asset topologies.

Good-to-Have

• Experience with GenAI / LLM-powered knowledge graph retrieval (GraphRAG, Vector + Graph hybrid indexing, LangChain / LlamaIndex).
• Familiarity with Data Security Posture Management (DSPM), Cloud Security Posture (CSPM), or asset inventory discovery tools.
• Experience working in regulated financial services environments handling sensitive customer and transactional data.
• Relevant certifications in Data Science, Cloud Data Engineering (GCP Professional Data Engineer, Azure Data Scientist / Data Engineer), or Cyber Security (CISSP, GDAT, or equivalent).