Back
Remote role and popular Data Scientist title raise applicant density, but niche security/LLM skills moderate competition.
Role requires specialized ML/AI, LLM, and security-domain experience, limiting cross-industry transferability.
Explicit seven-year requirement plus mandatory ML/LLM, production deployment, and Python stack increases filter strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead end-to-end data science projects including problem framing, production deployment with CI/CD, monitoring, and operational optimization of AI systems.
Design and implement security-focused AI agents, advanced machine learning models (predictive modeling, anomaly detection, classification), and adapt large language models for domain-specific security use cases.
Provide technical leadership and mentorship across multidisciplinary data science initiatives, partnering closely with Product, Engineering, and Security teams to deliver measurable business impact.
M.Sc. in Data Science, Computer Science, Statistics, Mathematics, or related quantitative discipline.
At least 7 years of hands-on data science experience delivering end-to-end solutions into production.
Strong expertise in Python and modern data science tools (NumPy, Pandas, Scikit-learn, PyTorch/TensorFlow).
Solid understanding of ML theory, practical model behavior, and LLM architectures with fine-tuning methodologies.
Experienced in developing production-grade AI capabilities, particularly in security-focused contexts involving complex data and policy management.
Demonstrated ability to lead complex multidisciplinary initiatives with technical rigor and strategic impact within product-driven environments.
Familiarity with advanced ML topics such as ML observability, model monitoring, graph technologies, generative AI approaches, and domain-specific LLM adaptation is highly advantageous.