Data Scientist
About the Team:
Arctic Wolf Labs is the research-focused division at Arctic Wolf focused on advancing innovation in security operations. The mission of Arctic Wolf Labs is to develop cutting-edge technology and tools that are designed to enhance the company’s core mission to end cyber risk, while also bringing comprehensive security intelligence to Arctic Wolf’s customer base and the security community-at-large. Leveraging the more than two trillion security events the Arctic Wolf Security Operations Cloud ingests, parses, enriches, and analyzes each week, Arctic Wolf Labs is responsible for performing threat research on new and emerging adversaries, developing advanced threat detection models, and driving improvement in the speed, scale, and detection abilities of Arctic Wolf’s solution offerings. The Arctic Wolf Labs team comprises security and threat intelligence researchers, data scientists, and security development engineers with deep domain knowledge in artificial intelligence (AI), security R&D, as well as advanced threat offensive and defensive methods and technologies.
About the role
Data is a critical part of Arctic Wolf Network’s mission to solve cyber risk, where we process and analyze hundreds of billions of events every day to look for malicious and risky behavior. Furthermore, we’re geeks at heart and are passionate about data and solving cybersecurity problems for our customers. The AI & Data Science team works on analyzing Arctic Wolf’s product platforms, internal business challenges, and customer pain points to develop new products and features for our customers.
As a Security Data Scientist on the AI & Data Science team you will be responsible for developing data-driven solutions leveraging cutting-edge advanced analytics techniques. This involves working closely with engineering, product, operations, and other teams to understand the most important technical and business analytics needs to drive security-centric analytics outcomes. A successful candidate is self-motivated with a passion for excellence and attention to detail.
About you
Proven track record in the research, data science and engineering around building and shipping machine learning or statistical models that scale to high volumes of data (billions of data points)
Proficiency with Python and SQL
Experience using key ML libraries including scikit-learn, tensorflow, pytorch, sparkml
Experience with data fusion and data cleansing techniques for creating labelled data sets
Experience with developing synthetic data sets using generative and statistical modelling techniques
Understanding of classical ML and DNN modelling techniques for supervised and unsupervised learning and sequence modelling.
Understanding of 3 of either PGMs, RL, GNN, statistical modelling, or time series modelling
Understanding of ensembles and multi modal machine learning techniques to solve complex problems
Familiarity of large-scale distributed systems and related technologies such as Spark, Elasticsearch, Kubernetes, etc. is a plus
Familiarity with cloud platforms (we use AWS here) and automation technologies (e.g., Kubernetes, Jenkins, Chef, etc.) is a plus
Familiarity with the information/cyber security domain is a plus
Responsibilities
Analyze and correlate complex data from multiple sources
Perform exploratory data analysis to gain deeper understanding of the data
Work with stakeholders, including domain experts and product, to understand use cases and domain-specific problems and identify opportunities for applying machine learning to drive outcomes
Develop tools and algorithms for generating synthetic data sets
Define KPIs to measure model performance
Develop and test statistical and machine learning models for efficacy and operational impact
Write production quality code and work with other software engineering teams to deploy models into production
Support deployed models as a subject matter expert
Be creative and engineer novel features and methods to push beyond our current capabilities
Security Requirements
Conducts duties and responsibilities in accordance with AW’s Information Security policies, standards, processes, and controls to protect the confidentiality, integrity, and availability of AW business information assets.
Must pass a criminal background check and an employment verification as a condition of employment.