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Data Science

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Microsoft

9d ago

  • Job
    Full-time
    Senior Level
  • Data
    IT & Cybersecurity

AI generated summary

  • You need a relevant degree and 1-7 years of data science experience, including managing data and applying statistical techniques. Equivalent experience is also accepted.
  • You will solve business problems using data science, develop models, mentor engineers, ensure data integrity, engage stakeholders, and drive customer adoption of AI/ML solutions.

Requirements

  • 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 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 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 equivalent experience.
  • Bachelor's degree in data science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 7+ years 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 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
  • OR Doctorate 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 equivalent experience.

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. Describes the primary objectives of the team from a business perspective. Produces a project plan to specify necessary steps required for completion. Assesses current situation for resources, risks, contingencies, requirements, assumptions, and constraints. Coaches less experienced engineers in standards and best practices. Uses his or her understanding of organizational dynamics, interrelationships among teams, schedule constraints, and resource constraints to effectively influence partners to take action on insights. Understands 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. Engages business stakeholders to capture and shape their thinking on data-driven methods applicable to their value chain. Leads customer conversations to understand, define, and solve business problems.
  • Acquires data necessary for successful completion of the project plan. Proactively detects changes and communicates to senior leads. Develops useable data sets for modeling purposes. Contributes to ethics and privacy policies related to collecting and preparing data by providing updates and suggestions around internal best practices. Contributes to data integrity/cleanliness conversations with customers.
  • Leverages knowledge of machine learning solutions (e.g., classification, regression, clustering, forecasting, NLP, image recognition, etc.) 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, stability. Develops operational models that run at scale through partnership with data engineering teams. Coaches less experienced engineers on data analysis and modeling best practices. Develops a strong understanding of the Microsoft toolset in artificial intelligence (AI) and machine learning (ML) (e.g., Azure Machine Learning, Azure Cognitive Services, Azure Databricks). Breaks down complex statistics and machine learning topics into manageable topics to explain to customers. Helps the Solution Architect and provides guidance on model operationalization that is built into the project approach using existing technologies, products and solutions, as well as established patterns and practices.
  • Understands relationship between selected models and business objectives. Ensures clear linkage between selected models and desired business objectives. Assesses the degree to which models meet business objectives. Defines and designs feedback and evaluation methods. Coaches and mentors less experienced engineers as needed. Presents results and findings to senior customer stakeholders.
  • Uses business knowledge and technical expertise to provide feedback to the engineering team to identify potential future business opportunities. Develops a better understanding of work being done on team, and the work of other teams to propose potential collaboration efforts. Coaches and provides support to teams to execute strategy. Leverages capabilities within existing systems. Shares knowledge of the industry through conferences, white papers, blog posts, etc. Researches and maintains deep knowledge of industry trends, technologies, and advances Actively contributes to the body of thought leadership and intellectual property (IP) best practices.
  • Writes efficient, readable, extensible code from scratch that spans multiple features/solutions. Develops technical expertise in proper modeling, coding, and/or debugging techniques such as locating, isolating, and resolving errors and/or defects. Understands the causes of common defects and uses best practices in preventing them from occurring. Collaborates with other teams and leverages best practices from those teams into work of their own team. Mentors and guides less experienced engineers in better understanding coding and debugging best practices. Builds professional-grade documents for knowledge transfer and deployment of predictive analytic models. Leverages technical proficiency of 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.
  • Collaborates with end customer and Microsoft internal cross-functional stakeholders to understand business needs. Formulates a roadmap of project activity that leads to measurable improvement in business performance metrics over time. Influences 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.
  • Applies a customer-oriented focus by understanding customer needs and perspectives, validating customer perspectives, and focusing on broader customer organization/context. Promotes 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. Builds 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.
  • Embody our culture and values.

FAQs

What is the primary focus of the Data Science position?

The primary focus is on solving complex problems through data-driven methods, collaborating closely with customers, and delivering innovative solutions while emphasizing security within the Industry Solutions Organization.

What qualifications are required for this job?

A Bachelor's degree in data science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or a related field, along with 5+ years of data science experience, is required. Alternatively, a master's degree with 3+ years of experience or a doctorate with 1+ year of experience is acceptable.

What skills are preferred for the Data Science role?

Preferred skills include strong experience in managing both structured and unstructured data, proficiency in statistical techniques, and familiarity with machine learning solutions and programming languages like Python or R.

What type of environment should I expect in this role?

You can expect a fast-paced, customer-focused environment with a strong emphasis on delivering security solutions, collaborating with a dynamic team, and continuous learning about new technologies.

Is experience in cybersecurity important for this position?

Yes, strong cybersecurity experience is crucial as the role involves addressing complex customer scenarios and ensuring the security of Microsoft’s enterprise customers.

What are the key responsibilities of a Data Scientist in this role?

Key responsibilities include understanding business problems, data preparation, modeling and statistical analysis, evaluation of models, identifying industry opportunities, coding and debugging, business management, and maintaining customer/partner orientation.

Will I have opportunities for professional growth?

Yes, you will have the opportunity to work with a world-class team and experience mentorship that will contribute to your professional growth and career advancement.

Is there a collaboration aspect to this role?

Absolutely! You will collaborate with cross-functional stakeholders and other teams to understand business needs and propose collaborative efforts to enhance project outcomes.

Does this position involve mentoring?

Yes, mentoring and coaching less experienced engineers and stakeholders in data analysis and modeling best practices is part of the responsibilities.

What is the company's stance on diversity and inclusion?

Microsoft is an equal opportunity employer, and they promote a diverse workforce with a commitment to inclusivity in hiring practices, ensuring all qualified applicants receive consideration without regard to various protected characteristics.

Technology
Industry
10,001+
Employees
1975
Founded Year

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