Data Scientist II

Responsibilities

• Understand problems facing projects and is able to leverage knowledge of data science to be able to uncover important factors that can influence outcomes on specific products.
• Describe the primary objectives of the team from a business perspective. Produces a project plan to specify necessary steps required for completion.
• Assess current situation for resources, risks, contingencies, requirements, assumptions, and constraints.
• Coach less experienced engineers in standards and best practices.
• Use their understanding of organizational dynamics, interrelationships among teams, schedule constraints, and resource constraints to effectively influence partners to take action on insights.
• Understand business strategy briefings and articulates data driver strategies for specific industries or cross-industry functions, such as: Sales/Marketing, Operations, and new Data Monetization Schemes.
• Engage business stakeholders to capture and shape their thinking on data-driven methods applicable to their value chain.
• Lead customer conversations to understand, define, and solve business problems.
• Collaborate with end customer and Microsoft internal cross-functional stakeholders to understand business needs.
• Formulate a roadmap of project activity that leads to measurable improvement in business performance metrics over time.
• Influence stakeholders to make solution improvements that yield business value by effectively making compelling cases through storytelling, visualizations, and other influencing tools.
• Exemplifies and enforces team standards related to bias, privacy, and ethics.
• Industry and Research Knowledge / Opportunity Identification.
• Use business knowledge and technical expertise to provide feedback to the engineering team to identify potential future business opportunities.
• Develop a better understanding of work being done on team, and the work of other teams to propose potential collaboration efforts.
• Coach and provides support to teams to execute strategy.
• Leverage capabilities within existing systems.
• Share knowledge of the industry through conferences, white papers, blog posts, etc.
• Research and maintains deep knowledge of industry trends, technologies, and advances.
• Actively contributes to the body of thought leadership and intellectual property (IP) best practices.
• Data Preparation and Understanding. Acquires data necessary for successful completion of the project plan. Proactively detects changes and communicates to senior leads.
• Develop useable data sets for modeling purposes.
• Contribute to ethics and privacy policies related to collecting and preparing data by providing updates and suggestions around internal best practices.
• Contribute to data integrity/cleanliness conversations with customers.
• Apply a customer-oriented focus by understanding customer needs and perspectives, validating customer perspectives, and focusing on broader customer organization/context.
• Promote and ensures customer adoption by delivering model solutions and supporting relationships. Works with customers to overcome obstacles, develops tailored and practical solutions, and ensures proper execution.
• Build trust with customers by leveraging interpretability and knowledge of Microsoft products and solutions. Helps drive realistic customer expectations, including information about the limitations of their data.
• Write efficient, readable, extensible code from scratch that spans multiple features/solutions.
• Embody our culture and values.

Qualifications

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field
  • OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) or consulting experience
  • OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 2+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.
• 3+ years of experience with SQL, R, Python to implement statistical models, machine learning, and analysis (Recommenders, Prediction, Classification, Clustering, etc.) in big data environment.
• 3+ years of experience with written and verbal communication to educate and work with cross functional teams.
• 3+ year of experience in delivering on ambiguous projects with incomplete or imperfect data

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.

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
• • OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
  • OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.
• Experience taking sample data and scaling it up to create an estimate of the population.
• Experience benchmarking between metrics to calculate relative performance or share.
• Experience exploring to find new data and leveraging that to stand up new metrics.