Senior Delivery Data Science

Om jobbet

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

• Leverages subject matter expertise to analyze problems and issues facing projects to uncover, manage, and/or mitigate factors that can influence final outcomes across product lines. Partners with business team to drive strategy and recommend improvements. Raises opportunities to look for new work opportunities, project and engagements and different contexts to use existing work. Establishes, applies, and teaches standards and best practices. Contributes to the development of the project plan for the Data Science portions and identifies risks, assumptions, issues, and dependencies to meet the customer requirements and comply with their constraints. Leads the Data Science portions of customer conversations to define data-based strategies to achieve the customer's business outcome. Effectively handles customer escalations and steers "Red Status" projects back to "Green." Communicates changes needed to Product Engineering using Field Feedback mechanisms.

Data Preparation and Understanding

• Oversees data acquisition efforts and ensures data is properly formatted and accurately described. Utilizes key technologies and tools necessary for data exploration (e.g., SQL, Python). Uses querying, visualization, and reporting techniques to explore the data, including distribution of key attributes, relationships between attributes, simple aggregations, properties of significant sub-populations, and statistical analyses. Mentors and coaches engineers in data cleaning and analysis best practices. Identifies gaps in current data sets and drives onboarding of new data sets (e.g., bringing on third-party data sets). Drives discussions around ethics and privacy policies related to collecting and preparing data. Integrates industry-wide ethics insights and best practices to influence internal processes and drive decision-making. Builds data platforms from scratch across products. Builds Data Science business solutions using existing technologies, products and solutions as well as established patterns and practices. Provides guidance on model operationalization of models created by data scientists. Identifies new opportunities from data and process data in a way that is usable for general purpose. Actively contributes to the body of thought leadership and intellectual property (IP) on best practices for data acquisition and understanding. Leads and resolves data integrity problems.

Modeling and Statisical Analysis

• Generalizes machine learning (ML) solutions into repeatable frameworks (e.g., modules, packages, general purpose software) for others to use. Exemplifies and enforces team standards related to bias, privacy, and ethics. Evaluates the methodology and performance of teammates' models and (as appropriate) recommends solutions for improvement. Anticipates the risks of data leakage, the bias/variance tradeoff, methodological limitations, etc., and is able to guide teammates on solutions. Drives best practices relative to model validation, implementation, and application. Develops operational models that run at scale. Partners with others to identify and explore opportunities for the application of machine learning and artificial intelligence (AI) predictive analysis. Identifies new customer opportunities for driving transformative customer solutions with ML modeling. Incorporates best practices for ML modeling with consideration for AI ethics. Develops deep expertise in specialized areas by staying abreast of current and emerging methodologies an AI and ML. Is expert 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. Drives best practices relative to model validation, implementation, and application by evaluating the work of others and recommends solutions for improvement, as needed. Develops deep expertise in specialized areas and stays abreast of current and emerging methodologies an AI and ML. Collaborates with the Solution Architect to provide guidance on model operationalization that is built into the project approach using existing technologies, products and solutions as well as established patterns and practices. Uses technologies, products and services created by Microsoft Product Engineering and/or open source equivalents supported on Microsoft Azure. Leverages technical expertise of modeling techniques (e.g., linear regression, multiple regression, decision-tree building, neural network generation, support machines, derivatives) to select appropriate tool to complete project objectives. Interprets models according to knowledge, success criteria, and desired test design.

Evaluation

• Conducts thorough review of data analysis and modelling techniques used to summarize the process review and highlight areas that have been missed or need reexamined. Utilizes results of the assessment and process review to decide on next steps (e.g., deployment, further iterations, new projects). Identifies new evaluation approaches and metrics and invents new methodologies to evaluate models.

Industry and Research Knowledge/Opportunity by Idenitifaction

• Tracks advances in industry and academia, identifies relevant state-of-the-art research, and adapts algorithms and/or techniques to drive innovation and develop new solutions. Researches and maintains deep knowledge of industry trends, technologies, and advances. Leverages knowledge of work being done on team to propose collaboration efforts. Proactively develops strategic responses to specific market strengths, weaknesses, opportunities, threats, and/or trends. Mentors and coaches more junior engineers in data analysis best practices. Serves as subject matter expert and role model for junior engineers. Identifies strategy opportunities. Actively contributes to the body of thought leadership and IP best practices by actively participating in external conferences. Researches and maintains deep knowledge of industry trends, technologies, and advances.

Coding and Debugging

• Independently writes efficient, readable, extensible code/model that spans multiple features/solutions. Contributes to the code/model review process by providing feedback and suggestions for implementation and improvement. Develops expertise in proper modeling, coding, and/or debugging techniques such as locating, isolating, and resolving errors and/or defects. Leads a project team in the gathering, integrating, and interpreting of data/information from multiple sources in order to properly troubleshoot errors. Provides feedback on non-optimized features/solutions back to product group, and explores potential for new features. Leverages expert-level proficiency of big data software engineering concepts, such as Hadoop Ecosystem, Spark, CI/CD, Docker, Delta Lake, MLflow, AML, and REST API Consumption/development. Expert-level proficiency in big data software engineering concepts.

Business Managment

• Defines business-strategy goals, customer-strategy goals, and solution-strategy goals. Partners with teams to identify and explore opportunities for the application of machine learning and other data science tools. Leverages technical expertise to develop partnerships between product teams, Sales teams, Area teams, and Services. Work collaboratively across disciplines. Leads involvement of intellectual property (IP) definition improvement. Coaches and mentors more junior engineers.

Customer/Partner Orientation

Commits to a customer-oriented focus by acknowledging customer needs and perspectives, validating customer perspectives, focusing on broader customer organization/context, and serving as trusted advisor. Identifies opportunities and adds valuable insight by incorporating an understanding of the business, product/service functionality, data sources, methodologies to reframe problems, and the customer perspective. Interprets results, develops insights, and effectively communicates results to customer. Leads the discussion with customers and offers pragmatic solutions that is feasible given their data limitations.
Qualifications:

Required/Minimum Qualifications

• Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 10+ 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 7+ 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 5+ years data science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
  • OR equivalent experience.
• 3+ 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 12+ 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 10+ 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 8+ 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 .

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