Responsibilities

Ideate new opportunities where Data Science can assist and solve problems for various internal business units, particularly around building recommendations through site, newsletter, social media, and other relevant touchpoints as needed.
Partner with business and translate business and analytics strategies into multiple short-term and long-term projects, and drive end-to-end execution technically.
Identify solutions from existing research and apply them efficiently for business problems.
Build quick prototypes to check feasibility and value to business and present to business.
Lead the way in the entire value chain of a project/initiative life cycle - interface with business, understand the requirements/specifications, gather data, prepare it, train, validate, test the model, create business presentations to communicate insights, monitor/track the performance of the solution, and suggest improvements.
Coordinate model deployment in production by working with the ML engineering team.
Guide the team in implementing well-researched approaches to data preparation, data modeling, and experimentation.
Be a continuous and self-motivated learner and mentor junior data scientists to take up new certifications, competitions, pet projects, etc.
Explain the business insights in a manner that business can understand it easily.
Partner with other data teams - data engineering/services/BI teams/Marketing science teams for common initiatives, goals, etc.

Qualifications

At least 5+ years of hands-on experience in Machine learning & deep learning implementations and 8-10 years of overall experience (with a background in software engineering being a plus).
Experience in deploying models in production and tracking it for performance degradation.
Master’s degree with an emphasis in a quantitative discipline such as statistics, engineering, economics, or mathematics. PhD preferred.
Exceptional Communication Skills - verbal, written.