Are you interested in building personalized recommendation for billions of users, especially in finance domains?

Finance Recommendation team in Content Service organization is building personalized recommendation in finance domains in various product, including MSN and Edge default home page, etc. Our team focus on whole recommendation stack building, especially the modeling parts in different recommendation layers, including document understanding, segment recall, user profile modeling, personalized ranking, diversity optimization, etc.

If you’re looking for one team to utilize your ML skills to optimize user engagement of real product, grow your ML skills by iterating against users’ feedbacks and resolving real product challenges, then this is the team for you!

As one applied scientist in the team, your major responsibilities including:

• Thinking through the product scenarios and goals, identify key challenges and design experimental process for iteration and optimization.

• Offline model training and optimizing, collaborating with platform team on online serving and perf optimization for shipping.

  • Keeping on track of research trends in the fields of personalized recommendation, deep learning and AI.

  Work independently and collaboratively with other

• Master / PhD in computer science or related fields focus on machine learning and AI.

• 3+ years’ experiences in areas of machine learning, natural language processing or large-scale data mining.

• Solid coding skill and good experience on deep learning framework like PyTorch, TensorFlow, CNTK, etc.

Preferred:

• Working / research experiences on recommendation areas is good plus.

Good communication skills and self-motivated.