As the Advanced Analytics team expands to further LogRhythm’s commitment to data-driven security, we are looking to bring on a strong technical Data Scientist with experience and passion for building and shipping product.  This team implements the next generation of data-driven algorithms that detect and classify large-scale network behavior to defend against cybersecurity attacks and data breaches. LogRhythm is a leading provider of unified security intelligence and analytics solutions that empower organizations to automate the detection, prioritization and neutralization of cyber-threats. Our highly scalable platform collects, classifies and contextualizes petabytes of machine and forensic data from across the extended IT environment. 

Opportunity

The Advanced Analytics team operates like a startup within LogRhythm, rapidly developing compelling prototypes for new data-driven product opportunities and working hands-on with other engineering teams to bring them to market. This is an ideal role for a Data Scientist with modern infrastructure expertise to deliver scalable, reliable data pipelines, the engineering skills necessary to bring data-driven products to market and statistical and machine learning depth-of-knowledge to push our next generation of algorithm development.

Responsibilities:

• Designing scalable infrastructure and pipelines for ingesting, persisting, and processing large volumes of potentially streaming data
• Working hands-on/embedded with engineering teams to develop and rapidly deliver scalable, reliable, performant product that provides security value to end-users
• Providing thought leadership on product direction and implementation based on modern machine learning and statistical techniques
• Researching and rapidly demonstrating proof-of-concept data-driven algorithms
• Solving open-ended problems and sharing knowledge within a small, collaborative team; communicating and demonstrating technical ideas to stakeholders and engineers 

Qualifications

• 2+ years demonstrated success solving data science problems on modern infrastructure in a product-focused, engineering setting
• Advanced degree in relevant technical field (Computer Science, Mathematics/Statistics, or similarly relevant engineering or computational discipline) or Bachelor’s degree with significant industry experience
• 1+ year(s) experience using large-scale data processing stacks (Spark, Hadoop, or similar; distributed databases) for ETL and analytics workflows
• 1+ year(s) experience with provisioned and on-demand cloud computing platforms such AWS EC2/Lambda
• Proficiency in a distributed computing environment operating on Linux platforms
• 1+ year(s) experience using open source machine learning toolkits (scikit-learn, caret, TensorFlow, or similar) with the ability to modify or implement algorithms as necessary
• 2+ years experience with one or more high-level languages (Python, R, Scala, or similar)
• 1+ year(s) experience with standard version control systems (Git) and best practices
• Ability to rapidly prototype data visualizations with web frameworks commonly used for data science (D3, shiny, or similar)