GET TO KNOW OKTA

Okta is The World’s Identity Company. We free everyone to safely use any technology—anywhere, on any device or app. Our Workforce and Customer Identity Clouds enable secure yet flexible access, authentication, and automation that transforms how people move through the digital world, putting Identity at the heart of business security and growth.
At Okta, we celebrate a variety of perspectives and experiences. We are not looking for someone who checks every single box - we’re looking for lifelong learners and people who can make us better with their unique experiences.
Join our team! We’re building a world where Identity belongs to you.

BASIC QUALIFICATIONS

• Bachelor’s degree in Computer Science, Engineering, Statistics or a related quantitative field.
• Fluency in a computing language, e.g. Python, Scala, C++, Java, etc.
• Experience with building production systems and platforms at scale.
• Familiar with full AI/ML lifecycle from model development, training, testing, deployment, monitoring, and refining and iterating.
• Knowledge in handling large datasets using SQL and databases in a business environment.
• Excellent verbal and written communication.
• Exceptional troubleshooting and problem solving skills.
• Thrive in a fast-paced, innovative environment.

PREFERRED QUALIFICATIONS

• Knowledge of AWS Redshift, Snowflake or similar databases.
• Experience with data workflow platforms such as Airflow, and container technologies such as Docker and Kubernetes.
• Familiar with Python and machine learning/data science libraries such as Scikit-learn and Pandas for analyzing and modeling data.
• Familiar with multiple machine learning algorithmic methodologies, such as decision trees, logistic regression, Bayesian analysis, and others.
• Superior verbal and written communication skills with the ability to advocate technical solutions effectively to data scientists, engineering teams and business audiences.
• Ability to deal well with ambiguity, ability to self-motivate, prioritizing needs, and delivering results in a dynamic environment.
• Combination of deep technical skills and business savvy to interface with all levels and disciplines within our and our customer’s organizations.
  

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• Design and implement infrastructure and platform components for training, deploying, and monitoring machine learning models in production.
• Build pipelines to ingest data from myriad sources into a centralized data lake for various use cases.
• Collaborate with production engineering teams to ensure that machine learning models integrate successfully into production environments while adhering to performance and availability SLOs.
• Participate in project planning, design, development, and code reviews.
• Communicate verbally and in writing to business customers and leadership teams with various levels of technical knowledge, educating them about our systems, as well as sharing insights and recommendations.
• Partnership across Engineering, Product Management, Security and Design teams to solve technical and non-technical challenges.