Data Scientist - MLOps

工作内容

We are looking forward to hire "Data Scientist - MLOps" who thrives on challenges and desires to make a real difference in the business world.

With an environment of extraordinary innovation and unprecedented growth, this is an exciting opportunity for a self-starter who enjoys working in a fast-paced, quality-oriented, and team environment.

What you should have:
Minimum 3-5 years of experience in Data Science.
Partnered with the Business and Technical Groups to understand the questions they are trying to address, to unlock value, and the data they have or need to collect to address the question.
Experience in Engaging regularly to refine opportunities and develop a pathway for value-generating data projects in the appropriate Business Units.
Provide specialist advice and act as the first point of contact for data science queries.
Assist in designing data capture / experimental setups for exploratory work, or changes in process/data capture in existing systems.
Work with the Data Scientist to develop statistical models, algorithms, and/or machine learning algorithms to analyze data and address a particular business question.
Assist and supervise Data Engineers in developing and deploying production workflows for taking data in real-time or periodically from business functions, passing the data through the developed models, and producing the relevant reporting without human intervention.
Support reporting from analytics tools and developed models.
Support in architecting data warehouse and data lake.
Work with the Data Scientist to design and implement training and deployment approaches for data science and machine learning model components.
Develop and deliver a CI/CD pipeline + monitoring for the deployed models.
Perform solution research and rapid iterations of development to refine posed problems and identify potential solutions.

What you will do?

Develop a state-of-the-art data science and ML runtime stack in a multi-cloud environment.
Partner with leaders in the area and have insights to select off-the-shelf components vs building from the scratch.
Convert the proof of concepts to production-grade solutions that can scale for hundreds of thousands of users.
Be hands-on where required and lead from the front in following best practices in development and CI/CD methods.
Own delivery of features from top to bottom, from concept to code to production.
Develop tools and libraries that will enable rapid and scalable development in the future.
Architect key paradigms, pipelines, and other mechanisms to take ML systems from proof of concepts to realities in Production.
Successfully devise and implement strategies to ensure ML heavy systems operate with high accuracy in Production and adapt to discovered needs.
Lead on software engineering and software design for ML components.
Understand and use computer science fundamentals, including data structures, algorithms, computability and complexity, and computer architecture.
Manage the infrastructure and pipelines needed to bring models and code into production.
Research and implement best practices to improve existing machine learning infrastructure.
Collaborate with data engineers, application programmers, and data scientists.