Applied Scientist II

We are looking for an Applied Scientist II who is excited to work on ML/DL models capable to work at internet scale. The ideal candidate will have good Machine/Deep Learning knowledge with hands on experience in solving complex real-world ML/DL problems. In addition, the candidate will have knowledge of one or more ML/DL frameworks and a deep understanding of the different types of algorithms and their applications with hands-on experience in implementing at least a few of the solutions. We hire people excited to solve the hard problems, eager to work in a larger team of data scientists and engineers and ready to make an impact on how the web works.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Required Qualifications:

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
  • OR equivalent experience.
•  3+ years of industry work experience in Python, SQL, R, machine/deep learning, and analysis (Recommenders, Prediction, Classification, Clustering, etc.) in big data environment.

Preferred Qualifications :

• Experience with programming, e.g. Java, C#.
• Experience on large scale computing systems like COSMOS, Hadoop, MapReduce and/or similar systems.
• Familiarity with deep learning toolkits like - PyTorch, TensorFlow, etc.
• Self-driven and ability to deliver ambiguous projects with incomplete or dirty data.

• Ability to conceptualize, design and build an efficient data pipeline that can train, test and deploy high quality models into production to run at scale.
• Partner with teams to identify and explore opportunities for the application of machine learning and predictive analysis.
• Communicate with technical and non-technical audiences and contribute modelling expertise as a team player.