Senior Applied Scientist

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We are the Data & Applied Sciences team for Microsoft Teams, based in Bangalore, India. Our mission is to leverage advanced analytics, data science, and the transformative power of ML/AI to drive decision-making, enhance product performance, and maximize business impact. Our focus is on utilizing data to tackle high-impact business challenges and opportunities within Microsoft Teams, with a special emphasis on developing state-of-the-art Recommendation Engine solutions.

We are looking for a Senior Applied Scientist who is passionate about leading and pushing forward cutting edge research and technology platforms for human communication understanding. You will work as part of a team that brings together talent in the areas of machine learning, natural language processing, recommenders, information retrieval, software engineering, and trustworthy computing. We value and encourage diversity in the belief that it leads to both great workplaces and great products.

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 4+ 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 3+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate 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 equivalent experience.

• 4+years experience in Data Science / Machine Learning, with a strong background in Recommendation Systems.

• Expertise in Exploratory Data Analysis, Inference Analysis, and Predictive Analysis (Regression, Classification, etc.), with a strong preference for experience in Recommendation Engines.

Other Requirements:

Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:

• Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Preferred Qualifications:

• Ph.D. in Computer Science, Engineering or related field.
• Publications in major HCI/ML/IR/NLP conferences. Example: CHI, NeurIPS, ICML, KDD, WWW, SIGIR, WSDM, ACL, EMNLP.
• Awareness and understanding of emerging research and technologies related to Recommendation Systems.
• Experience in large scale data mining and cloud computing.
• Analytical, problem solving, programming and debugging skills.
• Effective verbal, visual and written communication skills.

You will be expected to work on many levels - from mining massive datasets for identifying opportunities, through designing and implementing your solutions into our offline and live production systems, defining appropriate metrics to measure their effectiveness, conducting rigorous A/B experiments and impartially evaluating their results. Your coding skills will be challenged writing scalable, distributed and highly efficient components. Candidates should give evidence of their ability to write efficient and maintainable code.

You will be involved in:

• Build models and featurization pipelines, enable feedback loop.
• Measure and improve the impact of models and work with product teams on appropriate designs of AI-powered experiences.
• Setting up, running and analyzing experiments.
• Prototyping new approaches and developing new algorithms, ML techniques such as modelling using deep learning, unsupervised ML approaches and privacy preserving data mining techniques.
• Improving metrics, feedback systems and reinforcement learning techniques.
• Working with other scientists, engineers and UX experts on the detailed design and implementation of end-to-end solutions, including data-pipelines for machine learning, deployments, performance monitoring and analysis, and continual refinement via feedback loop from real users.
• Staying current on technology trends and scientific developments in relevant areas inside and outside of the company, including attending academic and industrial conferences.
• Partnering with Development teams, Microsoft Research and Product Teams.