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Skilling Data Intelligence Lead
Lensa
Redmond (WA)
On-site
USD 119,000 - 235,000
Full time
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Job summary
A leading company is seeking a Data Skilling Intelligence Lead to harness AI in developing personalized learning experiences. The role involves extensive collaboration, data preparation for AI models, and creating actionable strategies to enhance employee skill development. Candidates with advanced degrees in data science and extensive experience in AI and data analytics are preferred.
Qualifications
- 5+ years experience managing structured and unstructured data.
- Experience in AI-enabled skilling in a matrixed global organization.
- Strong analytical and problem-solving skills.
Responsibilities
- Collaborate with HR and Engineering teams to leverage AI.
- Evaluate program success and effectiveness.
- Develop scalable pipelines using tools like Azure and Power BI.
Skills
Education
Tools
Job description
United States Army
Expeditors
SUGAR MOUNTAIN RESORT
The Boeing Company
Amazon
Amazon.com Services LLC
Amazon
Digitechlabs
Creative Solutions Services, LLC
SonSoft Inc.
Proliance Surgeons, Inc.
LVI Associates
University of Washington
Amazon
Amazon
Menlo Ventures
U.S. Navy
Amazon Kuiper Manufacturing Enterprises LLC
Amazon
The Global Learning and Skilling team is dedicated to revolutionizing employee skilling through AI-driven, personalized experiences. By integrating diverse learning data with the principles of human motivation, the team strives to create tailored skilling opportunities seamlessly integrated in the flow of work. Thus, readying data for AI processing is a cornerstone of this transformation.
In this critical role, you will collaborate closely with HR and Engineering teams to leverage AI in identifying essential data sets and converting insights into actionable strategies. These strategies are designed to personalize and contextualize learning, stimulate curiosity, and foster in the flow skill development.
The Data Skilling Intelligence Lead will assess experiences and evaluate program success and effectiveness to ensure they adapt to the evolving needs of employee skill development.
Responsibilities
Business Understanding and Impact
- Understands problems facing projects and is able toleverage 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 practices. Uses 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.
Data Preparation and Understanding
- Acquires data necessary for successful completion of the project plan. Proactively detects changes and communicates to 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 practices. Contributes to data integrity/cleanliness conversations with customers.
Evaluating for Insight and Impact
- 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 customer stakeholders.
Industry and Research Knowledge/Opportunity Identification
- Uses business knowledge and technical expertise to provide feedback to the engineering team to identify potential future business opportunities. Develops a betterunderstanding 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) practices.
Business Management
- 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.
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 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.
Additional Responsibilities
Program Management: Direct AI-driven data insights programs to align with business goals, collaborating across teams and promoting data-informed decisions.
Data Strategy & Insights: Define a data vision supporting personalized AI while ensuring compliance with privacy and responsible AI principles.
Telemetry & Behavioral Signals: Track key user behaviors and contexts to evaluate engagement and optimize AI interventions.
Data Collection & Infrastructure: Develop scalable pipelines using tools like Azure and Power BI, enabling real-time and batch data processing.
AI Readiness & Model Support: Prepare datasets for AI models, monitor performance, and enable continuous learning through user feedback.
Analytics & Insights: Create dashboards to analyze trends, track KPIs, and translate insights into actionable priorities for teams.
Other
- Embody our culture and values
Qualifications
Required 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)
OR 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)
2+ years experience in AI-enabled skilling in a matrixed global organization.
Preferred Qualifications
7+ years of experience in data science, analytics, or related fields.
Proven analytical skills with the ability to solve complex learning and skillingdata challenges.
Effective communication and stakeholder management abilities.
Experience within a large tech organization or matrixed environment, preferably in an HR context.
Familiarity with Microsoft Azure AI and data services.
Knowledge of behavioral analytics, people analytics, or enterprise productivity.
Understanding of AI-native enterprise learning systems.
Data Science IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $158,400 - $258,000 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay
Microsoft will accept applications for the role until June 25, 2025.
Microsoft is an equal opportunity employer. Consistent with applicable law, all qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations (https://careers.microsoft.com/v2/global/en/accessibility.html) .
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