HR Operations Analyst

Tokyo Electron•Austin, TX

About The Position

TEL US HR Technology team is looking for an HR Data Analyst with a vision for strategy, deep customer focus, brilliant consulting capabilities and impactful analytical skills. Our team advises the HR Leaders through data-based insights and compelling storytelling to achieve business objectives and people priorities, enables deep insights for the HR Leaders supporting key people and organization decisions and initiatives, and partners to grow data capability and enable modern data solutions that enhance our HR Centers of Excellence (CoE) organizations’ work with data. In this strategic role, you will partner with HR CoE Leaders, to understand the business and people priorities while discovering ways to measure and analyze their big bets related to Talent Acquisition, Talent Management, & Total Rewards priorities. You will be comfortable leading and consulting through ambiguity, presenting and consulting with HR Leaders across the Company and is a creative problem solver that recognizes opportunities. You will have the ability to think strategically at a global level and be able to effectively problem solve and project manage across all layers of the organization. All with the highest focus for customer obsession.

Requirements

Bachelor's Degree in Statistics, Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Information Technology or related field AND 5+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis OR Master's Degree in Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field OR equivalent experience.

Nice To Haves

5+ years applied work experience using analytical methods to solve business problems, driving improved decision-making and outcomes for Human Resources preferred.
Customer focus and project management skills – able to interact and contract professionally with a diverse group of executives, managers, and subject matter experts.
Passionate about client relationships and optimizing impactful analysis-based deliveries with the opportunity to train and promote thoughtful self-serve solutions.
Presentation skills: building meaningful slides, presenting them effectively to executive level clients and management, responding to questions on-the-fly based on in-role business context and acumen.
Attention to detail and high commitment to accuracy and auditing.
Proficiency in technology including Excel, PowerBI, and Workday Reporting and Discovery Boards as well as coding languages such as SQL, R or Python

Responsibilities

Design an analytical approach and deliver research and findings using data, visualizations, and effective storytelling techniques.
Translate findings into succinct, actionable business insights, often in coordination with partners to help you tell a holistic story.
Apply a breadth of tools and analytical techniques to answer a wide range of high-impact people-related business questions.
Solve sometimes ambiguous business problems by formulating and facilitating solutions that realize the needs and provide for the requirements arising in the organization.
Exhibit effective execution by effectively contracting and communicating with customers, partners, and co-workers to define and provide deliverables, timelines, and responsibilities, all the while balancing and prioritizing across multiple and sometimes shifting priorities.
Experimentation and Innovation Supports and/or consults with other teams on the build and execution of formal experiments or develops prototypes/proofs of concepts, to evaluate the impact of new or changed features or processes.
Partners cross-functionally to advise on experimental design or evaluation frameworks for established data sources, as well as decisions related to data use, personalization, and the interpretation of results.
Takes calculated risks and applies learnings, reports results, and identifies relevant data-driven recommendations and/or connections to results of other work.
Expertise in Data Identifies and leverages appropriate sources of data to address specific b needs.
Evaluates data for applicability to answering relevant and impactful business questions, determines methods for integrating data across sources, and addresses data integrity and/or quality issues independently and/or in partnership with other teams.
Contributes to the development and/or recommendation of initial/prototype data models and/or tools for consumption by others and escalates complex issues with those models and/or tools to appropriate Engineering or Data-Science teams.
Orchestration and Collaboration Works within and across teams to ensure alignment in data sources, methods, models, analytical tools and processes, and business priorities to deliver key insights and results.
Engages stakeholders to identify and act on opportunities to leverage resources, data sources, and solutions that were instrumental to success in similar contexts and consults across teams on decisions related to data sourcing, analyses, and the interpretation of analytical results.
Customer/Stakeholder Orientation Leverages understanding of data and the business to examine projects through a customer- and/or stakeholder-oriented focus.
Manages customer and/or stakeholder expectations regarding project/product progress and timeline and takes responsibility to enhance customer excellence.
Assists and learns from executive team members to interpret results, develop insights, and communicate results to customers and/or stakeholders.
Possesses a basic understanding of model accuracy and its dependency on data quality and can articulate these topics in relevant terms in customer or stakeholder discussions.
Data Analysis Determines appropriate analytical and inferential techniques to address business questions, executes analyses and interprets results with actionable recommendations, and partners to build on others' analyses and frameworks.
Critically evaluates the choice of tools, techniques, and assumptions to ensure they are appropriate within context and provides feedback on features and functions of analytical tools and/or models.
Data Privacy and Governance Stays abreast of data privacy requirements, responsible and ethical data handling practices, and models compliance with classification and governance rules and regulations.
Ensures data have undergone appropriate Corporate, Executive, and Legal Affairs (CELA) reviews and ensures work products are in alignment with principles and controls and is aware of where to seek further expertise on data privacy rules and regulations when needed to determine the impact of updated guidance on work activities and results.
Reporting and Sharing Results Shares and simplifies relevant findings and insights to tell stories of analyses through one or more means including dashboards, reports, data visualizations, self-service platforms, slides, internal forums, ad-hoc inquiries, and/or talking points.
Ensures presented results are accessible, provides information accurately and clearly, and appropriately speaks to the needs of the intended audience(s).
Recommends and makes improvements to insights reporting as appropriate.
Business and Data Landscape Develops depth of understanding of the business, its data landscape, and the lineage of those data across multiple areas.
Considers relevant data sources and external trends, anticipates data requirements, and probes for insight to understand business- or data-related topics beyond stated concerns.
Identifies connections and themes across first and/or third-party data sources and anticipates opportunities to leverage those connections and themes to contribute to the development of data infrastructure and analytical frameworks that enable the evaluation of business questions.
Improvement and Efficiency Recommends efficiency improvements for core work related to analytics and reporting that are reusable, self-service, and directed to meaningful interpretation of data and driving business decisions.
Makes recommendations for efficient methods for conducting ad-hoc analyses, and whether to make them standard.
Shares domain knowledge to create clarity, support readiness to consume and leverage data and/or insights and evaluate the viability of automated methods for use in data collection, reporting, and/or analysis.
Model Evaluation Understands linkages between analytical model(s) and business objectives.
Assists with testing models on test applications.
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.
If model is deemed deficient, seeks to determine why.