Senior Data Scientist

Design, develop, train, and deploy high-performance machine learning models for critical security tasks such as threat detection, anomaly scoring, and behavioral analytics.

Conduct feature engineering and selection on vast, high-velocity streams of security data (logs, network telemetry, endpoint data).

Own the model lifecycle, including versioning, rigorous testing, and continuous improvement through MLOps best practices.

Stay up-to-date on state-of-the-art research in data science, deep learning, and security-specific AI to drive platform innovation.

Explore novel statistical methods and machine learning techniques to tackle emerging and sophisticated cyber threats.

Design and implement A/B testing and evaluation frameworks to measure the impact and performance of deployed models.

Partner with Data Engineers to define, preprocess, and structure large, complex datasets for model training and inference.

Implement and manage data pipelines specifically for feature extraction and model serving.

Leverage vector databases and RAG (Retrieval-Augmented Generation) pipelines for enhanced security context and large language model applications.

Work closely with Security Operations to understand real-world threat landscapes and ensure model outputs are actionable and integrated into workflows.

Collaborate with Product Managers to define AI features and translate model performance into measurable business value.

Present complex analytical findings and model behavior clearly to technical and non-technical audiences.

5+ years of professional experience in Data Science, Machine Learning Engineering, or a related quantitative field.

Strong theoretical and practical experience with a wide range of ML models (e.g., classification, clustering, time-series, deep learning).

Proficiency in Python and its data science ecosystem (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow).

Expertise in SQL and experience working with large-scale data warehouses (Snowflake, BigQuery, or Redshift).

Demonstrated experience with MLOps principles, tools, and platforms (e.g., Kubeflow, MLflow, Airflow/Dagster for orchestration).

Solid understanding of probability, statistics, and experimental design.

deploying and maintaining models in a cloud environment (GCP or AWS).

Excellent communication skills with the ability to drive projects autonomously and translate business needs into technical requirements.

Prior experience applying data science/ML in the cybersecurity or security analytics domain, particularly in MDR or MSSP environments.

with real-time / streaming data systems (Kafka, Pub/Sub, Kinesis) for low-latency threat detection.

Familiarity with the use of Vector Databases and RAG architectures.

with modern analytics engineering frameworks.

working in an early-stage startup environment.

Master’s or Ph.D. in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field (or equivalent experience).

Relevant certifications in Data Science or Cloud ML Platforms are a plus.

Us?

Opportunity to work with cutting-edge AI-driven cybersecurity technologies and Google SecOps solutions.

Collaborate with a talented and innovative team focused on continuously improving security operations.

Competitive salary and benefits package.

A culture of growth and development, with opportunities to expand your knowledge in AI, cybersecurity, and emerging technologies.