Staff Machine Learning Engineer

Job Description
What you get to do in this role:
We’re not yesterday’s IT department, we're Digital Technology. The world around us keeps changing and so do we. We’re redefining what it means to be IT with a mindset centered on transformation, experience, AI-driven automation, innovation, and growth. We’re all about delivering delightful, secure customer and employee experiences that accelerate ServiceNow’s journey to become the defining enterprise software company of the 21st century. And we love co-creating, using, and highlighting our own products to do it.
Ultimately, we strive to make the world work better for our employees and customers—when you work in ServiceNow Digital Technology, you work for them.
You will play a major part in building AI and Machine Learning (ML) solutions that transform the user experience and workflow efficiency of enterprise services. Act as a resource to the Machine Learning team with regards to Data Lifecycle Management. We are taking a completely fresh approach with the expectation that anyuser, regardless of technical knowledge, can use the AI/ML solutions we develop to operate the services in the enterprise setting in a thoughtful and scalable manner.We are just getting started with our early-adopter customers and we need your help in building and making available an amazing range of solutions to our 5k+ enterprise customers around the world.
ML Engineering:
Develop and maintain a knowledge of real-time and snapshotted data sources from across the company, as well as Analytics team members responsible for various sources.
Work with the Data Scientists to develop and implement key machine learning models
Facilitate ML data preparation and scoring, and support the ML strategy for the Enterprise Data Platform to optimize predictions and recommendations
Act as a resource to the Machine Learning team with regards to Data Lifecycle Management.
Snowflake:
Design scalable, reusable data models in Snowflake while aligning with Analytics architecture and deployment processes to ensure policy compliance
Experience in Implementing performance tuning and query optimization
Experience in building customer facing products using “Data As a Service” concepts using REST API
Experience in Dimension modeling to cater to self service reporting
Experience in using snowflake connector from Databricks to read and write data from Snowflake.