Intuit Staff DS/ML Engineer Data Exchange 

Israel, Center District, Petah Tikva 

559841926

In this role, you will:

• Lead and apply best practices in ML Project lifecycle management: scoping, data preparation, deployment, and monitoring
• Collaborate with multiple teams at Intuit and contribute to components in different business units. We love engineers who lead the change, communicate with customers, and deliver the most beautiful and intuitive applications
• Be expected to help architect, integrate, optimize, deploy, and code data pipelines and ML models at scale using the latest industry tools and techniques

• Design and build systems, which improve machine learning scalability, usability, and performance
• Own end-to-end model development lifecycle, including defining problems, featurization in distributed process systems, model exploration, and development, deployment, and model performance monitoring
• Collaborate with stakeholders to define success criteria and align model metrics with business goals
• Work side-by-side with product managers, business analytics, data scientists, and backend engineers in enabling AI solutions for business use cases
• Explore the state-of-the-art technologies and apply them to deliver customer benefits.
• Roughly 50% hands-on coding

• 6+ years industry experience bringing AI models from modeling to production
• Experience in modern, advanced analytical tools and programming
• Expertise and experience in data mining algorithms and statistical modeling techniques such as classification, regression, clustering, anomaly detection, and text mining
• Strong understanding of the Software design and architecture process
• Experienced with working in cloud production-grade high-scale microservices environment
• Languages such as Python, Java, or Scala
• Expertise with Spark for data manipulation and featurization
• Efficient in SQL, Hive, or SparkSQL
• Comfortable in Linux environment
• Familiar with backend services: K8S, Spring Boot, Vert.x, Docker

• Excellent oral and written English communication skills: demonstrated ability to explain complex technical issues to both technical and non-technical audiences
• BS, MS, or PhD in an appropriate technology field (Computer Science, Statistics, Applied Math, Operations Research), or equivalent work experience