Microsoft Data Science II 

United States, Washington 

71102247

Required 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 statisticaltechniques)
  • 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 statisticaltechniques)
  • OR equivalent experience.
• 1+ year(s) customer-facing, project-delivery experience, professional services, and/or consulting experience.

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.

Additional / Preferred Qualifications:

• 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).
• 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.

• Business understanding: Understands underlying business and product goals to inform design of data science solutions. Evaluates project plan for resources, risks, contingencies, requirements, assumptions, and constraints. Effectively communicates business goals, data insights, and data science solutions with variety of stakeholders.
• Data fluency: Explores, queries, visualizes, and processes data sets to deeply understand available datasets. Evaluates and leverages existing methodologies and tools, such as statistical and ML packages. Able to identify and propose solutions to data integrity and quality issues.
• Modeling & analysis: Understands pros/cons of wide variety of ML techniques (classification, regression, clustering, time series analysis, natural language processing, etc.) and algorithms (linear/logistic regression, gradient boosting, agglomerative clustering, deep neural networks, Transformer networks, etc.) to select the most appropriate solution. Applies standard modeling techniques (cross-validation, regularization, ensembling, etc.) as appropriate to ensure quality, reproducible results. Conducts well-designed experiments, performs statistically sound analyses, communicates results clearly and accurately to stakeholders (engineering and PM teams and leadership).
• Measurement & iteration: Measures success of data science solutions in the context of business impact and goals. Analyzes model performance and quickly iterates to improve performance metrics.
• Engineering skills: Writes efficient, scalable, maintainable code. Performs comprehensive quality checks for data processing and ML modeling code. Understands proper debugging techniques when dealing with data pipelines and non-deterministic code. Familiar with big data tooling, ETL pipeline principles, REST API consumption and deployment.
• Customer focus: Considers user experience when designing data science solutions. Examines and evaluates projects through customer-focused lens. Responsive to user feedback.
• Embody our