Data Scientist

Data Scientist Job Description Data Scientist — Machine Learning / Deep Learning (PyTorch or TensorFlow) Role summary 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. 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. What you’ll do 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. Required qualifications 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. Why join us 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.

Apply Now