LivaNova PLC is a global medical technology company built on 50+ years of experience and a Vision to change the trajectory of lives for a new day. Our Mission is to create ingenious medical solutions that ignite patient turnarounds.

At LivaNova, we understand the importance of bringing both clinical and economic value to our customers. We are a strong, market-leading medical technology and services company, offering a diverse product portfolio and global reach.

LivaNova is listed on the Nasdaq stock exchange under the ticker symbol “LIVN.” LivaNova has approximately 3,000 employees worldwide. We are headquartered in London (UK) and maintain a presence in more than 100 countries.

As a worldwide leader in cardiopulmonary and neuromodulation, we don’t just treat conditions—we aspire to alter the course of lives. So from patients seeking answers to impossible situations to clinicians striving to do their best work to employees finding a place where they can change the world—

Welcome to LivaNova. Welcome to your new life.

POSITION SUMMARY:

The HR Data Analyst, Talent Analytics will carry out overall strategy for employee data at LivaNova to support the need for data-driven decision-making regarding workforce productivity, engagement and better returns from recruitment and learning investments in the company.

KEY RESPONSIBILITIES:

Compile, structure, and analyze data from HRIS, payroll and Finance systems to develop insights and conclusions that help streamline HR processes throughout the company.

Work with the full data analysis pipeline including data cleaning, process documentation, and communicating results via report writing and visualization.

Participate actively in HR Business Intelligence including project intake, visualization tools, quality assurance, and storytelling. Interpret employee data to identify significant differences, relationships, and trends in data, as well as factors that could affect the results of research.

Identify statistical analysis techniques required to deliver insights.

Advance the use of complex analytical techniques and statistical thinking.

Design and implement data models using statistical principles (e.g., regression, correlation) and develop predictive models for attrition, performance, and recruiting demand.

Produce concise, story-driven presentations to communicate analytic insights.

Collect and share information on government labor statistics and external benchmarks.

Continually analyze competitor practices to make appropriate recommendations to upper management.

Partner with HR leaders to design research plans, develop hypotheses, collect data, and run various statistical models.

Help recruiters by providing relevant data, statistics, trends, and patterns to make the recruiting process more efficient. Similar for talent development, total rewards and employee experience (e.g. retention) practices.

Collaborate with subject matter experts across the HR service areas (e.g. Talent Development, Total Rewards and Talent Acquisition), as well as HR leadership, to promote data governance and stewardship, and to improve overall strategic and operational performance and insight.

Work with the HR business partner for organizational development (e.g. organization levels, structure, teams, span of control) and partner with colleagues and customers to achieve meaningful discussions regarding data outputs and feedback on usage activities to improve HR output.

Educate the HR team on analytical findings, general data and data management best practices.

Interact with professional and community groups related to analytics and business intelligence and maintain strong internal and external networks of peers to stay always ahead of the curve.

ESSENTIAL KNOWLEDGE & EXPERIENCE:

Master’s degree in Data Science, Computer Science, Business Analytics or closely related field

1 year of experience as a Human Resources Analyst or HR Data Analyst

The Master’s degree program must have included courses in Data Mining, Data-Driven Quality Management, Analytical Decision Modeling and provided technical hands-on experience with statistics, data analytics, data visualization (QLIK, Tableau or ggplot2), business intelligence, reporting and software experience with R or Python, a range of data types of various structures and sizes, and an understanding of the differences and use cases for interpretable vs. black box models.