Microsoft Data Scientist 

United States 

176696097

Required/Minimum 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.

Additional or 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.

• Leverages understanding of data science and business to examine a project and consider factors that can influence final outcomes within a technical area. Evaluates project plan for resources, risks, contingencies, requirements, assumptions, and constraints. Documents key business objectives. Effectively communicates business goals in analytical and technical terms. Consistently shares insights with stakeholders.

Data Preparation and Understanding

• Understands where to acquire data necessary for successful completion of the project plan. Utilizes querying, visualization, and reporting techniques to describe acquired data, including format, quantity, identities, and other surface properties. Explores data for key attributes and contributes to the development of data quality report describing results of the task, initial findings, and impact on the project. Collaborates with others to perform data-science experiments using established methodologies, statistics, optimization, and probability theory for general purpose software and statistical packages. Assesses different tools and techniques and selects the appropriate one. Serves as an effective partner in data preparation efforts to Solution Architects, Consultants, and Data Engineers. Adheres to Microsoft's privacy policy related to collecting and preparing data. Identifies data integrity problems.

Modeling and Statistical Analysis

• Leverages knowledge of machine learning solutions (e.g., classification, regression, clustering, forecasting, natural language processing [NLP], image recognition) and individual algorithms (e.g., linear and logistic regression, k-means, gradient boosting, autoregressive integrated moving average [ARIMA], recurrent neutral networks [RNN], long short-term memory [LSTM] networks) to identify the best approach to complete objectives. Understands modeling techniques (e.g., dimensionality reduction, cross-validation, regularization, encoding, assembling, activation functions) and selects the correct approach to prepare data, train and optimize the model, and evaluate the output for statistical and business significance. Understands the risks of data leakage, the bias/variance tradeoff, methodological limitations, etc. Writes all necessary scripts in the appropriate language: T-SQL, U-SQL, KQL, Python, R, etc. Constructs hypotheses, designs controlled experiments, analyzes results using statistical tests, and communicates findings to business stakeholders. Effectively communicates with diverse audiences on data-quality issues and initiatives. Understands operational considerations of model deployment, such as performance, scalability, monitoring, maintenance, integration into engineering production system, and stability. Develops operational models that run at scale through partnership with data engineering teams.

Evaluation

• Understands linkage between achieved model and business objectives. Assists with testing models on test applications and on real data or production data. Analyzes model performance. Incorporates implicit and explicit customer feedback into model evaluation. Conducts review of data analysis and modeling techniques to determine factors that may have been overlooked or need to be reexamined. Contributes to the summary of the review process.

Industry and Research Knowledge/Opportunity Identification

• Learns and understands the current state of the industry, including knowledge of tools, techniques, strategies, and processes that can be utilized to improve process efficiency and performance. Maintains knowledge of current trends within the discipline. Attends internal research conferences and participates in on-hands training, when appropriate. Actively contributes to the body of thought leadership and intellectual property (IP) best practices.

Coding and Debugging

• Understands existing code to write efficient and readable code of their own for a specific feature, seeking guidance as needed. Collaborates with other engineering teams to develop, test, and implement changes to optimize code to improve efficiency, reliability, diagnosability, maintainability, and operability of systems. Develops working expertise in proper debugging techniques such as locating, isolating, and resolving errors and/or defects. Collaborates with other engineers/project team members to integrate data models into customers' engineering systems. Understands big-data software engineering concepts, such as Hadoop Ecosystem, Apache Spark, continuous integration and continuous delivery (CI/CD), Docker, Delta Lake, MLflow, AML, and representational state transfer (REST) application programming interface (API) consumption/development.

Business Management

• Develops understanding of data structures and their relationship to Microsoft's customer business. Observes engineers and learns best practices in identifying growth opportunities, understanding strategy goals, customer- and product-strategy goals, and exploring opportunities for machine learning (ML) application, seeking guidance when needed. Understands business goals of the customer, per engagement basis.

Customer/Partner Orientation

• Leverages understanding of data science and business to examine projects through a customer-oriented focus. Manages customer expectations regarding project/product progress and timeline. Takes responsibility to enhance customer excellence. Assists and learns from team members interpret results, develop insights, and communicate results to customers. Possesses basic understanding about model accuracies' dependency on data quality and able to articulate it in customer discussions.