Data Science Manager

Quick Heal Technologies Ltd . is India’s most trusted name in cybersecurity innovation. With a legacy of over 30 years, we’ve grown from a garage-born antivirus start-up to a globally respected, publicly listed cybersecurity product company with a market capitalization of INR 2,500 Cr and revenues approaching INR 400 Cr. Quick Heal has transformed from protecting individual users under the Quick Heal brand to safeguarding businesses of every size with our enterprise-grade Seqrite portfolio. Our mission - Innovate. Simplify. Secure. drives everything, we do. We are home to India’s largest indigenous malware and threat research lab, and the creators of industry - first innovations like AntiFraud.ai and SiA , a Gen-AI powered analyst assistant. Our product stack spans Endpoint Protection, XDR, Zero Trust, Threat Intelligence, Data Privacy, and beyond - built upon a robust Cybersecurity Mesh Architecture. Quick Heal Academy (QHA) is a leading institute for cybersecurity education and training. We collaborate with universities and organizations to build skilled professionals and strengthen the global cybersecurity ecosystem. Quick Heal drives impactful CSR initiatives through its foundation, focusing on education, employability, vocational training, and cybersecurity awareness—contributing to a safer, inclusive, and sustainable future. Job Title: Data Science Manager – Cybersecurity Experience: 10+ years 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 master’s 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.