Data Scientist

We are seeking an experienced Data Scientist for Cyber Analytics & AI team to design, build, and deploy machine learning and deep learning solutions for client engagements. You'll lead end-to-end model development: data preparation, model design with PyTorch or TensorFlow, scalable training with distributed engines and production hand-off-working closely with engineers, consultants, and business stakeholders., * Design, develop, and validate ML/DL models using PyTorch or TensorFlow for real business problems.

• Implement production-ready code in Python and collaborate with engineering teams for deployment.
• Work with C/C++ or Java components where models are integrated into performance-sensitive systems.
• Process and transform large datasets using distributed computing frameworks (Dask/Ray).
• Lead model training, hyperparameter tuning, experiment tracking, and performance evaluation.
• Build reusable pipelines and components for feature engineering, training, and inference.
• Translate business use cases into technical solutions and present model findings to non-technical stakeholders.
• Ensure model reliability, monitoring, and compliance with governance and security requirements.
• Mentor junior team members; contribute to best practices, code reviews, and architecture decisions., * Work on high-impact and with global reach AI/ML projects across industries centered on cyber security.
• Collaborate with multidisciplinary teams and influence technical direction.
• Opportunity to mentor and grow within a leading analytics and AI practice.

This position requires a strong foundation in machine learning, deep learning, predictive modeling, and multi-modal AI and proven proficiency in Python and model deep learning frameworks., * 3-5 years hands-on experience building ML or deep learning models using PyTorch or TensorFlow.

• Strong Python programming skills; experience producing clean, well-documented, version-controlled code.
• Practical experience with C/C++ or Java for production integration, model optimization, or tooling.
• Experience with distributed computing engines (e.g., Spark/PySpark, Dask, Ray) for large-scale data processing.
• Solid understanding of core ML concepts: supervised/unsupervised learning, neural network architectures, regularization, evaluation metrics, and model validation.
• Experience with model training workflows, hyperparameter tuning tools, and ML tooling (e.g., MLflow, TensorBoard).
• Proven communication and interpersonal skills and experience working in cross-functional teams.

Preferred (nice-to-have)

• Experience with graph databases and graph ML (Neo4j, Amazon Neptune) or libraries like PyTorch Geometric.
• Background in cybersecurity use cases (threat detection, anomaly detection/fraud analytics).
• Familiarity with cloud platforms (AWS, Azure, GCP) and containerization/orchestration (Docker, Kubernetes).
• Exposure to MLOps practices: CI/CD for models, model monitoring, automated retraining.
• Advanced degree (MS degree or higher) in Computer Science, Statistics, Data Science, Applied Mathematics, computational sciences, or related field.