As a Data Science Manager, you will lead, mentor, and grow a team of applied data scientists working on machine learning models that power AI-driven cybersecurity solutions. You will own the end-to-end development lifecycle of AI projects, from exploratory research and experimentation through to scalable deployment and optimization. In this role, you will partner with engineering and product leadership to define technical strategies, prioritize initiatives, and ensure successful model delivery. You will also drive innovation across a broad spectrum of ML domains, including supervised/unsupervised learning, anomaly detection, and generative AI, while providing hands-on guidance and championing best practices in MLOps, data governance, and model evaluation.
Key Responsibilities
Lead, mentor, and grow a team of applied data scientists working on machine learning models that power AI-driven cybersecurity solutions.
Own the end-to-end development lifecycle of AI projects – from exploratory research and experimentation through scalable deployment and optimization.
Partner with engineering and product leadership to define technical strategies, prioritize initiatives, and ensure successful model delivery.
Drive innovation across a broad spectrum of ML domains including supervised/unsupervised learning, anomaly detection and generative AI.
Provide hands-on guidance in model development using tools such as scikit-learn, PyTorch, TensorFlow, and Hugging Face.
Ensure high-quality execution through strong code review practices, reproducibility, and model evaluation frameworks.
Champion best practices in MLOps, data governance, explainability, and monitoring.
Keep pace with academic and industry advances and translate cutting-edge research into productized capabilities.
Key Responsibilities
Lead, mentor, and grow a team of applied data scientists working on machine learning models that power AI-driven cybersecurity solutions.
Own the end-to-end development lifecycle of AI projects – from exploratory research and experimentation through scalable deployment and optimization.
Partner with engineering and product leadership to define technical strategies, prioritize initiatives, and ensure successful model delivery.
Drive innovation across a broad spectrum of ML domains including supervised/unsupervised learning, anomaly detection and generative AI.
Provide hands-on guidance in model development using tools such as scikit-learn, PyTorch, TensorFlow, and Hugging Face.
Ensure high-quality execution through strong code review practices, reproducibility, and model evaluation frameworks.
Champion best practices in MLOps, data governance, explainability, and monitoring.
Keep pace with academic and industry advances and translate cutting-edge research into productized capabilities.
Requirements:
8+ years of industry experience in AI/ML or Data Science, with at least 4-year leading teams and managing direct reports.
Solid proficiency in Python and machine learning libraries/frameworks such as scikit-learn, PyTorch, TensorFlow, or Hugging Face.
Strong familiarity with natural language processing (NLP), including LLMs and transformer-based architectures, and their applications to real-world use cases.
Strong technical communication skills and the ability to collaborate across cross-functional teams.
Strategic mindset with the ability to connect AI research to business value and product opportunities.
Preferred Qualifications
Masters degree or PhD in a technical field (e.g., Computer Science, Machine Learning, Statistics, Engineering).
Familiarity with cybersecurity, identity security, or risk mitigation use cases.
Experience with cloud-based ML infrastructure (e.g., AWS SageMaker, GCP Vertex AI) and big data tools (e.g., Spark, Airflow).
Hands-on knowledge of MLOps tooling for CI/CD, monitoring, and versioning of ML assets.
Publications, patents, or open-source contributions in AI/ML.
8+ years of industry experience in AI/ML or Data Science, with at least 4-year leading teams and managing direct reports.
Solid proficiency in Python and machine learning libraries/frameworks such as scikit-learn, PyTorch, TensorFlow, or Hugging Face.
Strong familiarity with natural language processing (NLP), including LLMs and transformer-based architectures, and their applications to real-world use cases.
Strong technical communication skills and the ability to collaborate across cross-functional teams.
Strategic mindset with the ability to connect AI research to business value and product opportunities.
Preferred Qualifications
Masters degree or PhD in a technical field (e.g., Computer Science, Machine Learning, Statistics, Engineering).
Familiarity with cybersecurity, identity security, or risk mitigation use cases.
Experience with cloud-based ML infrastructure (e.g., AWS SageMaker, GCP Vertex AI) and big data tools (e.g., Spark, Airflow).
Hands-on knowledge of MLOps tooling for CI/CD, monitoring, and versioning of ML assets.
Publications, patents, or open-source contributions in AI/ML.
This position is open to all candidates.
