Data Science - Architect

Job Description : Data Science Manager Cybersecurity

Location: Pune, Bangalore

Job Summary

We are seeking a Data Science Manager to lead a high-performing team of data scientists and ML engineers focused on building scalable, intelligent cybersecurity products. You will work at the intersection of data science, threat detection, and real-time analytics to identify cyber threats, automate detection, and enhance risk modelling.

Responsibilities

• Lead and mentor a team of data scientists, analysts, and machine learning engineers.
• Define and execute data science strategies aligned with cybersecurity use cases (e.g., anomaly detection, threat classification, behavioural analytics).
• Collaborate with product, threat research, and engineering teams to build end-to-end ML pipelines.
• Oversee development of models for intrusion detection, malware classification, phishing detection, and insider threat analysis.
• Manage project roadmaps, deliverables, and performance metrics (precision, recall, F1 score, etc.).
• Establish MLOps best practices and ensure robust model deployment, versioning, and monitoring.
• Drive exploratory data analysis on large-scale security datasets (e.g., endpoint logs, network flows, SIEM events).
• Stay current on adversarial ML, model robustness, and explainable AI in security contexts.

Required Qualifications

• Bachelor's or masters degree in computer science, Data Science, Statistics, or related field. Ph.D. is a plus.
• 7+ years of experience in data science or ML roles, with at least 2+ years in a leadership role.
• Strong hands-on experience with Python, SQL, and ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Experience working with security datasets: EDR logs, threat intel feeds, SIEM events, etc.
• Familiarity with cybersecurity frameworks (MITRE ATT & CK, NIST, etc.).
• Deep understanding of statistical modelling, classification, clustering, and time-series forecasting.
• Proven experience managing cross-functional data projects from conception to production.

Preferred Skills

• Experience with anomaly detection, graph-based modelling, or NLP applied to security logs.
• Understanding of data privacy, encryption, and secure data handling.
• Exposure to cloud security (AWS, Azure, GCP) and tools like Splunk, Elastic, etc.
• Experience with MLOps tools like MLflow, Kubeflow, or SageMaker.