Data Scientist

RESPONSIBILITY: Jigserv Digital is looking for Data Scientist to lead a Data Driven Architectures for both the Jigya Platforms as well as supervising some of our client projects in the space of Data Science and Analytics.

As a Data Scientist, you will have the opportunity to setup a robust data and machine learning infrastructure to develop models that impact millions of users and solve challenging business problems. You will work with engineers, and product managers to develop and iterate on models to help us grow our business.

The right resource will have the ability to build a world-class applications that are reliable, fast, responsive, and beautiful, delivering a breath-taking product experience to our users

Our Data Scientist will have the following attributes:

•            Displays a strong work ethic and sense of urgency.

•            Demonstrates strong initiative to make things happen.

•            Work with Product Management to plan and prioritize releases. Support product management in developing a product roadmap.

•            Run an effective Scrum process by facilitate Iteration Planning Meetings, Retrospectives and Daily Stand-ups

•            Figuring out the application infrastructure to support App Intelligence and Recommendations Engine.

Example Projects
 

• Showing the most relevant content in the users Feed based on his Interest, Location, Past behaviour with content and various other parameters.
• Identifying “Trending” Insights, Courses, Jobs, Events etc.

Qualification / EXPERIENCE

• 2+ years of industry experience developing Machine Learning Models with business impact — more experience preferred. 
• B.Tech, B.E, B.S., M.S., or PhD. in Statistics, Computer Science, Math, Physics, Economics, or other quantitative field 
• Deep understanding of AI / Statistics / Machine Learning, particularly Recommendation Systems, Search and Deep Learning techniques 
• Demonstrated programming abilities in Python and Numpy / Pandas 
• Experience productionizing Machine Learning models, in the cloud (GCP or AWS preferred)