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Senior Data Analyst III - 0125-US-010

liETtVLaARqgmMEbYzHNNLIzUPcdfPrwhYtVK7Qa.png Fast Facts

As a Senior Data Analyst III at Elsevier, you will evaluate AI models' performance, collaborate with cross-functional teams, and ensure the safety and effectiveness of generative AI solutions in healthcare.

liETtVLaARqgmMEbYzHNNLIzUPcdfPrwhYtVK7Qa.png Responsibilities: Lead model evaluation strategy, develop analytics frameworks, manage evaluations timelines, conduct quality assurance, and ensure compliance with regulations like HIPAA.

liETtVLaARqgmMEbYzHNNLIzUPcdfPrwhYtVK7Qa.png Skills: Expertise in AI/ML evaluation, data analytics, relational databases like SQL Server or Snowflake, BI tools like Tableau or PowerBI, and proficiency in Python for analysis.

liETtVLaARqgmMEbYzHNNLIzUPcdfPrwhYtVK7Qa.png Qualifications: 6-9 years of analytics experience in AI/ML, Bachelor's required; Master's or PhD preferred; proven leadership in analytics strategy.

liETtVLaARqgmMEbYzHNNLIzUPcdfPrwhYtVK7Qa.png Location: Home based in Pennsylvania, USA

liETtVLaARqgmMEbYzHNNLIzUPcdfPrwhYtVK7Qa.png Compensation: Not provided by employer. Typical compensation ranges for this position are between $100,000 - $140,000.



Do you like working with data and analytics to evaluate AI models and their performance?

Do you love collaborating with teams to assess and improve generative AI solutions?

About our Team

Elsevier is singularly focused on helping healthcare professionals improve clinical outcomes through evidence-based care. Our generative AI team works on evaluating and implementing cutting-edge AI technologies to enhance our healthcare solutions while ensuring their safety, reliability, and compliance with healthcare standards.

About the Role

We are seeking a motivated individual to join our dynamic generative AI evaluation team. We focus on assessing and validating AI models that support healthcare decision-making tools. Our team ensures that our AI solutions meet rigorous standards for accuracy, fairness, and clinical relevance. The successful candidate will help evaluate model performance, analyze output quality, and ensure our AI solutions align with healthcare best practices.

The successful candidate has relevant experience with AI model evaluation, including performance metrics analysis, output validation, and data visualization. They enjoy collaborating with cross-functional teams including AI researchers, product managers, clinicians, and engineers to evaluate AI models, analyze their outputs, and improve their performance in healthcare contexts.

Responsibilities

  • Leading requirements gathering and development of analytics frameworks for evaluating generative AI models in healthcare applications
  • Leading and defining the strategic direction for AI evaluation methodologies in their product area
  • Managing the analytics roadmap for model evaluation. Collaborating with internal and external stakeholders to manage evaluation timelines and deliverables
  • Defining, developing, and deploying queries and metrics to assess AI model performance and output quality
  • Leading data model creation and maintenance for AI evaluation frameworks and monitoring systems
  • Conducting comprehensive QA of AI outputs and participating in risk analysis and mitigation
  • Understanding how security and privacy laws such as HIPAA affect AI deployment in healthcare. Understanding how to handle sensitive data including PHI and PII in AI contexts

Requirements

  • 6-9 years of relevant experience in data analytics with focus on AI/ML evaluation
  • Bachelor's degree required, Master's or PhD preferred in Mathematics/Science
  • Demonstrated thought leadership in analytics strategy and enterprise-wide project implementation
  • Have experience in a data analytics role with focus on AI/ML evaluation
  • Have expertise in creating evaluation frameworks for large language models and other AI systems
  • Have expertise in using and querying relational database management systems such as Microsoft SQL Server, Snowflake or postgreSQL
  • Have expertise in using BI tools such as Tableau or PowerBI for AI performance visualization
  • Be able to demonstrate experience using Python for AI model evaluation and output analysis

Working for you

We know that your wellbeing and happiness are key to a long and successful career. These are some of the benefits we are delighted to offer:

- Health Benefits: Comprehensive, multi-carrier program for medical, dental and vision benefits

- Retirement Benefits: 401(k) with match and an Employee Share Purchase Plan

- Wellbeing: Wellness platform with incentives, Headspace app subscription, Employee Assistance and Time-off Programs

- Short-and-Long Term Disability, Life and Accidental Death Insurance, Critical Illness, and Hospital Indemnity

- Family Benefits, including bonding and family care leaves, adoption and surrogacy benefits

- Health Savings, Health Care, Dependent Care and Commuter Spending Accounts

- Up to two days of paid leave each to participate in Employee Resource Groups and to volunteer with your charity of choice

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Elsevier is an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law. We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form: https://forms.office.com/r/eVgFxjLmAK , or please contact 1-855-833-5120.

Please read our Candidate Privacy Policy.

Average salary estimate

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What You Should Know About Senior Data Analyst III - 0125-US-010, Elsevier

As a Senior Data Analyst III at Elsevier, you will play a pivotal role in evaluating the performance of AI models that are critically important in healthcare. Your days will be filled with opportunities to collaborate with diverse teams, ensuring that our generative AI solutions are safe, effective, and comply with healthcare regulations. You will be expected to take the lead in developing analytics frameworks, streamlining evaluation timelines, and conducting significant quality assurance tasks. Your expertise in AI/ML evaluation will enable you to analyze model performance and contribute to significant advancements in healthcare decision-making. You’ll collaborate with AI researchers, clinicians, and engineers to refine our AI models, making sure they meet rigorous standards for accuracy and clinical relevance. With 6-9 years of analytics experience under your belt, you’ll be expected to bring leadership and innovative strategies to our dynamic team. Your proficiency in SQL, BI tools like Tableau, and programming in Python will help you create insightful data visualizations and robust evaluation frameworks. Overall, your contributions will help to enhance healthcare outcomes and drive evidence-based care solutions. If you're passionate about data and eager to make a meaningful impact in the healthcare landscape, Elsevier would love to have you on board in this exciting remote role!

Frequently Asked Questions (FAQs) for Senior Data Analyst III - 0125-US-010 Role at Elsevier
What are the primary responsibilities of a Senior Data Analyst III at Elsevier?

The Senior Data Analyst III at Elsevier is responsible for leading the evaluation of AI models, developing and managing analytics frameworks, and conducting quality assurance processes to ensure compliance with healthcare regulations like HIPAA. This role involves collaborating with cross-functional teams to assess model performance and improve the effectiveness of generative AI solutions.

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What qualifications are required for the Senior Data Analyst III position at Elsevier?

Candidates for the Senior Data Analyst III role at Elsevier should possess a Bachelor's degree in Mathematics or a related field, with a Master's or PhD preferred. They should have 6-9 years of experience in AI/ML analytics, demonstrating leadership in developing analytics strategies and frameworks.

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What technical skills are essential for the Senior Data Analyst III role at Elsevier?

Essential technical skills for the Senior Data Analyst III role at Elsevier include proficiency in AI/ML evaluation, data analytics, and relational databases such as SQL Server or Snowflake. Additionally, familiarity with BI tools like Tableau or PowerBI and programming expertise in Python for data analysis are crucial for success in this position.

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How does a Senior Data Analyst III at Elsevier ensure compliance with healthcare regulations?

A Senior Data Analyst III at Elsevier ensures compliance with healthcare regulations by understanding the nuances of laws like HIPAA. This role involves conducting risk analysis, managing sensitive data appropriately, and ensuring that generated AI outputs meet regulatory standards to protect patient information.

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What can a candidate expect in terms of career growth as a Senior Data Analyst III at Elsevier?

As a Senior Data Analyst III at Elsevier, candidates can expect significant opportunities for career growth through exposure to cutting-edge AI technologies, involvement in cross-functional projects, and the chance to lead analytics strategies. Proficiency and thought leadership can lead to advanced roles in data analytics and AI evaluation as the company expands its innovative solutions in healthcare.

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Common Interview Questions for Senior Data Analyst III - 0125-US-010
Can you describe your experience with evaluating AI models in healthcare?

In your response, highlight specific projects where you evaluated AI model performance, detailing the metrics you used and the outcomes achieved. Discuss your role in collaborating with healthcare professionals and how your analysis improved the AI's effectiveness.

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What methodologies do you use to assess model performance?

Be prepared to discuss various methodologies, such as A/B testing, confusion matrix analysis, and ROC curves. Mention how these methods help in evaluating the accuracy, fairness, and reliability of AI models in healthcare contexts.

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How do you ensure data security and compliance with regulations like HIPAA?

Explain your approach to handling sensitive data, including privacy protocols you implement to ensure compliance with HIPAA. Share any specific examples where you identified risks and proposed mitigation strategies.

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How do you collaborate with cross-functional teams in your previous roles?

Discuss instances where you worked with professionals from various departments, emphasizing your communication skills and ability to align project goals. Mention how you utilized feedback from clinicians or engineers to enhance model evaluations.

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What BI tools have you used to visualize data for AI model evaluation?

Highlight your experience with BI tools like Tableau or PowerBI, and describe specific dashboards or reports you built to visualize complex data sets. Discuss how these visualizations contributed to stakeholders’ understanding of model performance.

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Can you provide an example of a challenge you faced in AI model evaluation and how you overcame it?

Share a specific challenge, such as dealing with incomplete data or conflicting outputs, and describe your analytical approach to resolve it. Focus on the steps you took to gather the necessary information and the tools you employed.

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What role do performance metrics play in your evaluation process?

Articulate how performance metrics are critical in evaluating AI models. Discuss the specific metrics you consider important and how they inform your assessments and recommendations for improving model outcomes.

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Describe your experience with programming languages such as Python in data analysis.

Be ready to discuss specific projects where you used Python, detailing libraries like Pandas or NumPy to manipulate data or SciKit-Learn for machine learning tasks. Provide examples of how your programming skills enhanced your analysis.

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What is your approach to leading an analytics roadmap for a project?

Explain your planning process, including how you define project goals, timelines, and deliverables. Highlight your capability to adapt the roadmap based on project requirements and stakeholder feedback to meet strategic objectives.

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How do you stay updated with the latest trends in AI and healthcare analytics?

Talk about the resources you utilize, such as industry publications, webinars, and professional networks. Mention any conferences or personal projects that help you stay informed and how you apply new knowledge to your work.

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Full-time, remote
DATE POSTED
April 10, 2025

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