Senior Data Analyst, Contingent

A Senior Data Analyst at Greenberg & Larraby, Inc. is responsible for analyzing complex data sets related to disability compensation, leading teams, managing budgets, and generating insightful reports. With a strong background in quantitative analysis and machine learning, you'll be key to driving data-informed decisions within the Federal sector.

To apply for the Senior Data Analyst position at Greenberg & Larraby, Inc., you need at least 15 years of experience in technical program management and quantified analysis, a Master's degree or higher in a statistics-related field, and a thorough understanding of disability compensation claims processes.

Greenberg & Larraby, Inc. offers a comprehensive benefits package for the Senior Data Analyst role, including health care plans (medical, dental, and vision), a retirement plan (401k, IRA), life insurance options, paid time off, and family leave programs, ensuring employees are supported in all aspects of their lives.

Experience in predictive analytics is essential for the Senior Data Analyst role at Greenberg & Larraby, Inc. This expertise helps you analyze past data to predict future outcomes, enabling the organization to make informed strategies and improvements.

Machine learning plays a crucial role in the Senior Data Analyst position at Greenberg & Larraby, Inc., as it allows for the automation of processes and deeper insights from data. This skill enhances your ability to identify trends and patterns that inform decisions related to disability compensation.

Yes, candidates for the Senior Data Analyst position at Greenberg & Larraby, Inc. must pass a thorough background check as part of the hiring process. This step ensures that we maintain the highest standards of integrity and security when working within the Federal sector.

The interview process for the Senior Data Analyst position at Greenberg & Larraby, Inc. is thorough and involves multiple stages, including a pre-screening call with a recruiter, interviews with team members, and assessments to understand your data analysis skills and fit within the organization.

When answering this question, focus on specific projects where you used machine learning, the models you implemented, and the outcomes you achieved. Discuss how machine learning improved efficiency, accuracy, or decision-making, showcasing your technical expertise.

This question requires specific examples of how you've managed budgets in past projects. Outline your approaches to tracking spending, forecasting expenditures, and ensuring project completion within budget, emphasizing your analytical skills and ethical responsibility.

Discuss your protocols for data validation, cleaning processes, and cross-checking information. Highlight any tools or software you've used and emphasize your commitment to delivering precise and reliable analyses.

Focus on a specific project and articulate the challenges faced, detailing the steps you took to overcome them. Emphasize your problem-solving abilities, critical thinking, and innovative approaches to data analysis.

Share the resources you use to stay informed, such as online courses, webinars, industry publications, and how you incorporate these learnings into your work. Show that you are proactive in your professional development.

List specific tools like SQL, R, Python, or data visualization software, and provide examples of how you've effectively used them in past roles. Be sure to mention any certification or training related to these tools.

Emphasize your communication skills and give examples of successful collaborations. Discuss how you ensure that data insights are understood and utilized by stakeholders that may not have a technical background.

Share your insights on how data analysis can improve the disability compensation claims process, focusing on efficiency, accuracy in processing claims, and the impact of informed decisions based on data.

Discuss the importance of defending your findings with data, how you would communicate effectively with stakeholders, and your willingness to revisit the data if necessary to maintain transparency and credibility.

Outline your approach to presenting data insights clearly and compellingly. Discuss the importance of storytelling with data and how you tailor your communication to suit different audiences to ensure your analyses drive impact.