Health Benefits Data Analyst

Health Benefits Data Analyst

Segal

Chicago, IL • Remote

$62.50K - $75K/yr

Other

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Job description

Are you passionate about delivering trusted advice that improves lives and using healthcare data analytics?
Do you enjoy digging into complex medical and pharmacy claims data, building SQL analyses, and translating results into client-ready insights?
Our opportunity may be for you.
Join Segal's Benefit Audit Solutions Practice as a Health Benefits Data Analyst.
The Health Benefits Data Analyst is a key contributor within a fast-growing segment of Segal's employee benefits business and an integral part of our client value proposition. In this role, you will perform extensive health claims data analytics using SQL and directly support client audit projects. Success requires comfort working across multiple client engagements at once and collaborating closely with consultants, auditors, client relationship managers, vendors, and clients to deliver high-quality, actionable results.
Essential Functions:
  • Perform extensive health claims data analytics utilizing SQL, including technical review of medical and health insurance contracts, financial arrangements, plan documentation, and evaluation of program effectiveness.
  • Collaborate with project teams to manage client assignments and service delivery initiatives (e.g., proposals, audits, negotiations, contract reviews) with a focus on quality, accuracy, and timeliness.
  • Support the growth of Segal's Benefit Audit Solutions Practice by contributing to business development initiatives, including proposal writing and service innovation.
  • 1-3 years of financial experience within a PBM and/or benefits consulting environment, including technical review and analysis of data, contracts, financial arrangements, and program effectiveness (highly preferred).
  • Bachelor's degree in business, actuarial science, mathematics, public healthcare, or a related discipline. Master's degree in healthcare informatics or another graduate-level health or data science discipline (highly preferred).
  • SQL coding (or other large data set manipulation experience) is required. Knowledge of Python is a plus.
  • Advanced knowledge of Microsoft tools including Excel, Word, and PowerPoint is required.
  • Strong analytical and problem-solving skills; demonstrated project management and multi-tasking ability; strong organizational and attention-to-detail skills; and effective written/oral communication and relationship-building skills.
  • Previous internships and general industry knowledge are a plus.
  • Knowledge of providers, products, services, data reporting, and contracting/negotiating elements and processes is preferred.
  • Intellectual curiosity and the aptitude to stay current as trends and technologies evolve.
Join Segal:
  • If your qualifications align closely with what we have described, we encourage you to apply. Your unique background and skills matter because at Segal, we believe that different experiences and perspectives drive innovation and excellence. We are committed to creating a fair and transparent hiring process and all hiring decisions will be merit driven. If you require accommodation during the interview, please let us know. Thank you for considering Segal. We are excited to learn more about you!
About Segal and its Total Rewards Program:
  • Segal is a privately owned, leading North American employee benefit, human resources and investment management consulting firm with over 80 years of history providing trusted advice that improves lives. Clients include public and private corporations, multiemployer trust funds, public sector entities, higher education institutions, institutional advisors, among many others.
  • Segal's total rewards are part of what makes Segal a special place to work. The current salary range for this position is $62,500 - $75,000.

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Segal logo

About Segal

Sourced by ZipRecruiter

Industry

Human resources consulting services

Company size

1,001 - 5,000 Employees

Headquarters location

New York, NY, US

Year founded

1939



Frequently asked questions

Q: What skills or qualities help someone succeed as a Data Analyst?

A: To succeed as a Data Analyst, key technical skills include proficiency in programming languages such as Python or R, expertise in data visualization tools like Tableau or Power BI, and knowledge of statistical analysis and machine learning concepts. Additionally, strong soft skills like effective communication, problem-solving, and collaboration are crucial for presenting insights to stakeholders and working with cross-functional teams. By combining these technical and soft skills, Data Analysts can drive business decisions, identify areas for improvement, and contribute to the growth and success of their organization.

Q: What is the career path for a Data Analyst?

A: A Data Analyst's typical career progression involves starting as an Entry-Level Data Analyst, where they collect, analyze, and interpret data to inform business decisions. As they gain experience, they can move into Mid-Level roles such as Senior Data Analyst or Business Analyst, where they take on more complex projects and lead smaller teams. Ultimately, they can advance to Senior Leadership positions like Data Scientist, Data Manager, or even Director of Analytics, where they oversee large-scale data initiatives and drive strategic business growth.\n\nKey opportunities for skill development and professional growth in this role include learning programming languages like Python or R, mastering data visualization tools like Tableau or Power BI, and staying up-to-date with emerging trends in machine learning and artificial intelligence. Additionally, Data Analysts can develop soft skills like communication, project management, and leadership to excel in their roles.\n\nLong-term career prospects for Data Analysts are diverse, with potential directions including transitioning into related fields like Business Intelligence, Data Engineering, or even becoming a Product Manager, or pursuing advanced degrees in Data Science or related fields to further specialize in areas like machine learning or data engineering.