Are you excited about creating innovative technology solutions? Would you like to use your Data Science knowledge to help people and organisations achieve more? Is sustainable AI development a passion of yours?

As organizations digitally transform their business and operating models, they look to us to provide them with the guidance and partnership that will help them achieve this change successfully and at pace. You will have the opportunity to collaborate and work closely with our broader ecosystem, including but not limited to engineering, product development groups, and research. This role offers and ample opportunity to experiment with state of the art technologies, gain expertise across several industry verticals and become a subject matter expert in the machine learning domain of your choice.

Most importantly, you will join a brilliant Team of like minded individuals, united by passion for Data Science and a common desire to support others, yet of versatile backgrounds and cultures. All voices are equally valuable in this group, distributed worldwide, and we are looking forward to listening to yours.

Working in an environment that empowers you to bring your best each day, you will grow professionally and personally as part of a team that care passionately for great customer outcomes, and who strive to improve the overall customer and partner experience.

Join our Team of exceptional people who deliver world-class cross industry customer innovation in an international environment. We value a supportive atmosphere with passion for growth, where you can earn customer confidence, trust, and loyalty by improving the overall customer and partner experience. At the same time, we put a specific emphasis on work-life balance, wellbeing, and the opportunity to operate in diverse and inclusive environment.

Responsibilities

Responsibilities

Business Understanding and Impact

• Understands 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 primary objectives of 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 junior 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 Engineering using Microsoft Consulting Services (MCS) Field Feedback mechanisms partners to take action on insights. Can explain the intersection of Data Science and Customer Business Strategy using the customer's language and in non-technical terms. Highlights changes needed to Product Engineering. 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 and define and solve business problems.

Data Preparation and Understanding

• Acquires data necessary for successful completion of 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.

Modeling and Statisical Analysis

• Leverages knowledge of machine learning solutions (e.g., classification, regression, clustering, forecasting, natural language processing, image recognition) and individual algorithms (e.g., linear and logistic regression, k-means, gradient boosting, ARIMA, RNN, LSTM) to identify the best approach to complete objectives. Understands modeling techniques (e.g., dimensionality reduction, cross validation, regularization, encoding, assembling, activation functions, etc.) 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 junior 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. Is proficient in the use of technologies, products and services created by Microsoft Product Engineering and/or open source equivalents supported on Microsoft Azure for the Modeling and Statistical Analysis of the gather data. Automates data quality checks and standardizes assessments, such as Python, R, C#, or similar programming languages. Uses technologies, products and services created by Microsoft Product Engineering and/or open source equivalents supported on Microsoft Azure.

Evaluation

• 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 junior engineers as needed. Presents results and findings to senior customer stakeholders.

Industry and Research Knowledge/Opportunity by Idenitifaction

• Uses business knowledge and technical expertise to provide feedback to the engineering team to identify potential future business opportunities. Develops 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 with 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 IP best practices. Maintains knowledge of industry trends, technologies, and advances in Data Science.

Coding and Debugging

• 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 junior 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, Spark, CI/CD, Docker, Delta Lake, MLflow, AML, and REST API Consumption/development. Exhibits technical proficiency in big data software engineering concepts.

Business Managment

• 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 story-telling, visualizations, and other influencing tools. Exemplifies and enforces team standards related to bias, privacy, and ethics.

Customer/Partner Orientation

• 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 customer to overcome obstacles, develop tailored and practical solutions, and ensuring proper execution. Builds trust with customer by leveraging interpretability and knowledge of Microsoft products and solutions. Helps drive realistic customer expectations, even of limitations of their data.

Other

• Embody our culture and values

Qualifications

Required/Minimum Qualifications

• 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 Doctoral 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 equivalent experience.
• 2+ years customer-facing, project delivery experience, professional services, and/or consulting experience.

Additional or Preferred Qualifications

• 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 Doctoral 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 equivalent experience.

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.

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