Observability Data Scientist at Elastic

At Elastic, we see endless possibilities in a world of data, and we use the power of search to help people and organizations turn that possibility into results. Elastic is the leading platform for search-powered solutions. With solutions in Enterprise Search, Observability, and Security, we help improve customer and employee search experiences, keep critical applications running smoothly, and protect against cyber threats. Elastic enables organizations worldwide to use the power of Elastic, including Netflix, Uber, BBC, Microsoft, and thousands of others.
Elastic was built on a foundation of being free and open, which trickles down to how we work. We’re a distributed organization and have been from the beginning. Being distributed isn’t just a way of doing business—it’s a mentality that is at the core of our culture.
Engineering philosophy
We believe that engineering complex, pluggable software for the web that is built to last the test of time is both tricky and exciting. Doing so requires a team of diverse individuals, with sharp minds and the ability to empathize with our users, working together with mutual respect and a common mission.
We care deeply about giving you full ownership of what you're working on. Our company fundamentally believes great minds achieve greatness when they are set free and are surrounded and challenged by their peers, which is clearly visible throughout our organization. At Elastic, hierarchy does not determine how decisions get made. We feel that anyone needs to be in the position to comment on anything, regardless of their role within the company.
The Role
The Observability team is in charge of developing solutions that focus on application developers and engineers that run infrastructure and services supporting these applications. Elasticsearch is an efficient datastore for logs, metrics, and application traces, supporting the three pillars of observability. 
Faced with an ever-increasing volume, variety and velocity of data generated by applications and infrastructure, Ops teams need to leverage big data and machine learning techniques to manage their applications and infrastructure. AIOps (Artificial Intelligence for IT operations) enhances a broad range of IT operations processes including anomaly detection, event correlation, and root cause analysis to improve monitoring, service management, remediation assistance, and automation tasks.
The team is looking for a Data Scientist who will be working in a team of developers and in collaboration with other data scientists to evolve our Observability product, leveraging AI on Logs, Metrics, APM, Synthetic Monitoring and other data to ensure the delivery a comprehensive solution that will help our users to reduce their mean time to detect (MTTD) and to repair (MTTR). 
You will collaborate with the broader Elastic Observability and Platform teams, a diverse set of software and machine learning engineers, experts in all the pillars of observability who lend domain expertise to work with you to creatively solve Observability problems. 
The team is diverse and distributed across the world, and collaborates on a daily basis over GitHub, Zoom, and Slack. Thus, the ability to work within a distributed team is critical.
What you will be doing

Joining the Actionable Observability team
Collecting and analyzing observability data to design and improve your models
Designing and developing models to solve observability problems
Collaborating with product managers and designers to leverage AIOps for Elastic Observability
Working with other engineers to get your data solutions implemented as features into the Observability product
Participating in collaborative efforts to improve our ML jobs with the ML teams
Promoting long-term vision for leveraging AI/ML techniques to creatively surface and solve Observability problems (senior position)

What you will bring along

Experience developing statistical and machine learning models
Solid understanding of supervised and unsupervised machine learning for classification, regression, clustering, anomaly detection, and NLP 
Proficient programming skills and experience using any of the most popular ML frameworks, e.g. Scikit-Learn, SpaCy, PyTorch, Keras, or TensorFlow
Experience working within engineering teams and being able to apply engineering solutions to data science problems
Ability to work in a fully remote and highly autonomous environment
Experience working with large volumes of time serie and unstructured data

What Would Be A Great Addition

Experience with observability, monitoring, and/or AIOps solutions
Experience using the Elastic Stack
Proficiency with Javascript and/or C++