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Home›Jobs›Datadog›Senior Applied Scientist
Datadog

About Datadog

The cloud monitoring platform engineers love

Key Highlights

  • Public company (NYSE: DDOG) - strong equity upside
  • 26,000+ enterprise customers including Netflix & Samsung
  • NYC headquarters with offices in Paris, Dublin, Sydney
  • $1.5B raised from Sequoia, IVP, and Index Ventures

Datadog (NYSE: DDOG) is a leading cloud observability platform that provides monitoring and analytics for applications, infrastructure, and logs. Trusted by over 26,000 customers including major companies like Netflix, Samsung, and Airbnb, Datadog is headquartered in New York City. The company went ...

🎁 Benefits

Datadog offers competitive salaries, equity options, generous PTO policies, and a flexible remote work policy. Employees also benefit from a learning ...

🌟 Culture

Datadog fosters an engineering-first culture, with 70% of its workforce comprising engineers. The company emphasizes a strong focus on solving complex...

🌐 WebsiteAll 413 jobs →
Datadog

Senior Applied Scientist

Datadog • Paris, France

Posted 1d ago🏢 HybridSeniorApplied scientist📍 Paris
Apply Now →

Skills & Technologies

Machine learningStatistical techniquesData analysis

Overview

Datadog is hiring a Senior Applied Scientist to design and build machine learning powered features within their platform. You'll work with algorithms for anomaly detection and data analysis, requiring expertise in machine learning and statistical techniques.

Job Description

Who you are

You have a BS/MS/PhD in Computer Science, Engineering, Machine Learning, or a related scientific field — your academic background has equipped you with a strong foundation in algorithms and data analysis. You possess experience in leveraging machine learning algorithms and statistical techniques to build scalable product features — your work has directly contributed to the success of previous projects. You are comfortable exploring and analyzing high volumes of data, and you can effectively communicate insights derived from your analyses to both technical and non-technical stakeholders. You thrive in collaborative environments, working closely with engineering and product teams to deliver impactful features. You are passionate about staying updated with the latest academic research in your field and enjoy sharing knowledge with your peers. You understand the importance of maintaining and monitoring models and infrastructure, ensuring that the systems you build are robust and reliable.

What you'll do

As a Senior Applied Scientist at Datadog, you will design solutions tailored to various use cases, focusing on building models and algorithms for machine learning powered features. You will research and benchmark relevant algorithms to identify the best fit for our applications, particularly in areas such as anomaly detection and error analysis. Your role will involve developing, deploying, and monitoring new and existing features in production, ensuring they meet the high standards expected by our users. You will participate in a journal club, reading and presenting the latest academic research papers to your team, fostering a culture of continuous learning and innovation. You will explore and analyze the data flowing through Datadog systems, telling the story behind the numbers and providing actionable insights. Additionally, you will maintain and monitor the models, services, and infrastructure owned by your team, participating in the on-call rotation to ensure system reliability.

What we offer

At Datadog, we value our office culture and the relationships that it fosters. We operate as a hybrid workplace, allowing you to create a work-life harmony that best fits your needs. You will have the opportunity to work with a talented team of professionals who are dedicated to building innovative solutions. We encourage you to apply even if your experience doesn't match every requirement, as we believe diverse teams build better products. Join us in shaping the future of monitoring and analytics in the cloud.

Interested in this role?

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