Principal Machine Learning Scientist

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

Are you someone with a passion for data, machine learning, insights, and technology? Do you want to work on one of the largest data science teams at Microsoft, lighting up actionable insights that drive key business decisions for the entire M365 organization? 
 
The Insights, Data Engineering & Analytics team (IDEAs), is a central data science team for M365 engineering and marketing. We are looking for a passionate, creative, analytical, and experienced data scientist who loves big data, is curious to explore, and who wants to impact business decisions. We are also striving to reform and cement how Data Science is done within an organization.

The team in which this role belongs to is in charge of looking at large, horizontal projects that are ambitious in nature. These projects will positively impact a multitude of teams and products within Microsoft. This team will also be responsible for researching and creating a set of best practices for all other Data Scientists to follow in their projects.

To be successful in this role you must be driven, self-directed, entrepreneurial, and focused on delivering the right results. You also need to have strong skills in written and oral communication, a can-do attitude, and the willingness to tackle hard problems in innovative ways. You must also thrive in a team environment that values cross team collaboration and building on the success of others.

Responsibilities

• Connect with various teams/products and uncover potential machine learning solutions that can generate massive impact
• Research, design, prototype, implement and test machine learning models/products
• Work with engineers to architect and develop operational models that 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 modeling expertise as a team player
• Construct Data Science best practices and guidelines to be followed by other Data Scientists

Qualifications

• Bachelor’s or Master’s degree or higher in Statistics/Math/Computer Science or related field
• 6+ years of industry work experience in SQL, R, Python to implement statistical models, machine learning, and analysis (Recommenders, Prediction, Classification, Clustering, etc.) in big data environment
• Experience with a variety of Data Science project types machine learning methods (i.e., forecasting, unsupervised, anomaly detection, clustering, imbalanced data sets, deep learning, tree-based methods, evolutionary algorithms, etc.)
  Familiarity with machine learning toolkits and libraries
• Exceptional written and verbal communication to educate and work with cross functional teams
• Be self-driven, and show ability to deliver on ambiguous projects with incomplete or dirty data.
• English and Portuguese languages mandatory

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.

Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.