Overview

With the recent addition of Prelert to Elastic, we now have a fully dedicated Machine Learning team in house. Our goal is to further expand our capabilities in developing technologies that allow users to better understand the behavior of their data. Our team has developed an unsupervised machine learning engine that can plow through large amounts of data and automatically find those insights our users today have been proactively finding using search. Current use cases include finding anomalies within transactions / operational metrics, detecting uncharacteristic user behavior, finding a population of attacking IP addresses but we are looking forward to expanding our capabilities to many more in the future. And in order to get there, our team is looking to hire an exceptional Data Scientist to join them. This is an amazing opportunity to join a small, highly experienced team where you can make an immediate impact and contribution to the core of our new machine learning offering.

Your Role:

As a Data Scientist you will contribute to the Machine Learning team within Elastic by performing data analysis on large and complex datasets and represent findings effectively and clearly to various audiences.

To succeed in this role, you will be driven by a genuine interest for understanding and exploring data. You must be able to interpret customer requirements and map these to deliver real business value. Our ideal candidate must strive to seek answers to questions about data that the customer has both asked for, or didn’t yet know they needed.

Typical projects you will be involved in will include:

  • Extracting valuable insight from real-world datasets. For example,
    • Understanding customer use cases
    • Transforming customer data
    • Analysing customer data using Prelert and/or other statistical tools
    • Examining Prelert models and results
    • Summarise dataset and valuable insights extracted from datasets.
  • Data extract, transform and load activity on large datasets
  • Generate test data sets for validating analytical functions and methods
  • Support engineers in development of big data anomaly detection analytic systems

Your Background:

  • BS, MS or PhD in an applied data science discipline such as Mathematics, Physics, Neuroscience, Machine Learning or similar
  • Solid practical experience (3+ years) as a Data Scientist
  • Proven ability to work with large data sets; analyze, explore and interpret results, present conclusions to peers
  • Experience with statistical packages and tools (e.g. R, SAS, Octave, MATLAB etc)
  • Experience with scripting languages (e.g. Unix Shell scripts,, Perl, Ruby, Python etc)
  • Experience with NoSQL or relational databases
  • Ability to work on a distributed team: work in a structured disciplined manner and document data and results effectively
  • Able to work collaboratively, good communication skills, personable
  • Knowledge, interest and a passion for topics in big data, machine learning, data mining and statistical analysis

Additional Information

  • Competitive pay, medical, dental, vision, disability, benefits
  • Stock options
  • Catered lunches, snacks, and beverages in most offices
  • An environment in which you can balance great work with a great life
  • Passionate people building great products
  • Employees with a wide variety of interests
  • Your age is only a number. It doesn’t matter if you’re just out of college or your children are; we need you for what you can do.
  • Distributed-first company with employees in over 27 countries, spread across 18 time zones, and speaking over 30 languages! Some even fly south for the winter 🙂

About Elastic

Elastic is on a mission to make real-time data exploration easy and available to anyone. As the company behind the popular open source projects Elasticsearch, Logstash, and Kibana, we're looking to hire team members invested in realizing this goal.

Founded in 2012 in Amsterdam by the people behind Elasticsearch and Apache Lucene, Elastic set forth a vision that search can solve a plethora of data problems. The origins of the company start back in 2010 when Shay Banon wrote the first lines of Elasticsearch and open sourced it as a distributed search engine.

With the rise of cloud computing and changes in IT infrastructure demanding requirements such as real-time search across infinite amounts of structured and unstructured data, Shay foresaw the need for a new type of software to solve today’s real-world data problems. Steven Schuurman, Uri Boness, and Simon Willnauer shared in Shay’s vision, joining forces to create the Elastic company we have today.

Since then, the creators of Kibana, Logstash, and Beats have joined the Elastic family, rounding out a product portfolio known as the Elastic Stack, which is used by millions of developers around the world. The Elastic family unites employees across 32 countries into one coherent team, while the broader community spans across over 100 countries.