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DatadogDatadog

Senior Applied Scientist

Senior Applied Scientist develops and productionizes machine-learning models and algorithmic features for high-volume streaming data in Datadog’s user-facing platform. The role requires experience with high-scale datasets, production data pipelines, statistical methods, and communicating technical concepts effectively.

About the job

Responsibilities

  • Design solutions for use cases by researching and benchmarking relevant algorithms.
  • Apply machine learning algorithms and statistical techniques to build scalable product features.
  • Develop, deploy, and monitor machine-learning features in production.
  • Analyze high-volume streaming data and communicate insights behind it.
  • Maintain and monitor team-owned models, services, and infrastructure.
  • Participate in journal club activities, including reading and presenting academic research.
  • Participate in the team’s on-call rotation.

Requirements

  • BS, MS, PhD, or equivalent experience in Computer Science, Engineering, Machine Learning, or a related scientific field.
  • Experience with high-scale systems and datasets, including building models, applying machine learning to real-world business problems, and writing production data pipelines.
  • Ability to explain complex ideas and algorithms to non-technical audiences.
  • Strong focus on code simplicity and performance.
  • Interest in building user-facing products.

Compensation and Benefits

  • Annual salary: $220,000–$275,000 USD.
  • Equity package including new-hire RSUs and an employee stock purchase plan.
  • Comprehensive benefits may include healthcare, dental, parental planning, mental health benefits, 401(k) matching, paid time off, fitness reimbursements, and employee stock purchase plan discounts.
  • Opportunities to collaborate across Datadog offices, attend conferences and meetups, and participate in mentorship and employee resource programs.

Skills

Machine Learning, Statistical Techniques, Anomaly Detection, Streaming Data, Data Pipelines, High-Scale Systems, Model Deployment, Model Monitoring, Python, Algorithms

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