Role Overview
We are seeking an experienced Data Scientist to join our AI & Cybersecurity team and drive the design, development, and deployment of advanced analytical and machine learning solutions for enterprise security use cases. The ideal candidate should have strong expertise in data science, ML engineering, and applied research, with the ability to work across massive security datasets, uncover meaningful threat patterns, and influence product direction.
You will play a key role in defining architecture, guiding junior team members, and partnering with cross-functional stakeholders to build scalable, intelligence-driven security capabilities.
Key Responsibilities
- Lead the end-to-end lifecycle of data science initiatives—from problem definition, data exploration, feature engineering, model development, and evaluation to deployment.
- Architect and implement ML solutions for threat detection, anomaly detection, behavior analysis, fraud detection, malware classification, and predictive security analytics.
- Design scalable pipelines for ingesting and processing large volumes of cybersecurity data (logs, telemetry, network traffic, endpoint and cloud signals).
- Drive research on emerging AI techniques (GenAI, LLMs, graph models, representation learning) to strengthen security analytics.
- Collaborate closely with engineering, product, and threat intelligence teams to operationalize ML/AI models into production environments.
- Conduct model performance reviews, bias checks, adversarial evaluations, and continuous monitoring strategies.
- Mentor AI/ML engineers and data analysts, fostering a culture of innovation and technical excellence.
- Present findings, insights, and architectural recommendations to leadership and cross-functional stakeholders.
- Stay ahead on developments in AI/ML and cybersecurity and contribute to the long-term roadmap and strategy.
Must Have Skills
Experience: 8–12 years
Qualification: Bachelor’s/Master’s/PhD in Computer Science, Data Science, AI/ML, Statistics, or related discipline
- Strong proficiency in Python and ML/data science libraries (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow).
- Deep expertise in supervised/unsupervised learning, deep learning architectures, NLP/GenAI, and anomaly detection.
- Proven experience working with large-scale, high-dimensional security datasets (SIEM logs, network telemetry, cloud logs, EDR data).
- Hands-on experience with building and deploying ML models in production environments (APIs, microservices, batch/stream pipelines).
- Solid understanding of data architecture, feature stores, distributed computing, and data pipeline design.
- Strong grasp of ML evaluation, statistical methods, experiment design, and model interpretability.
- Ability to translate ambiguous problems into structured analytical approaches.