Summary:

Prepare journal entries, account reconciliations and other analyses as part of the month end close process. Ensure that the monthly close for EmblemHealth is performed timely, accurately and in accordance with Generally Accepted Accounting Principles (GAAP). Participate in the development and enhancement of internal controls over financial reporting and process improvements to streamline the monthly close process. Support business processes by partnering with FP&A, IT, Operations, HR, and other internal business departments.

Responsibilities:

• Prepare monthly journal entries and account reconciliations as part of the month end close process.
• Ensure that journal entries, account reconciliations and other analyses are prepared in a complete, accurate and timely manner and that financial information is recorded in accordance with GAAP.
• Identify and implement process improvements to achieve a 10-business day close and to ensure compliance with corporate policy and MAR controls.
• Streamline the close process through automation to eliminate manual processes and improve efficiencies.
• Collaborate with other corporate groups to identify and implement "best practices" in accounting, reporting and financial controls.
• Provide supporting financial analysis and other documentation to internal business partners and prepare documentation in support of financial statements for internal and external auditors, as well as for CMS, DOH and DFS audits.
• Collaborate across departments to improve on the company's overall internal control structure and design and implement best practices.
• Perform duties as requested.

Qualifications:

• Bachelor's degree in Accounting required; MBA/MSA preferred
• CPA preferred
• 4 - 6+ years of relevant, professional work experience required
• 2+ years of experience in public accounting preferred
• Proficiency with MS Office (Word, Excel, Access, PowerPoint, Teams, Outlook), with advanced knowledge in Excel required
• Strong quantitative, data manipulation skills; ability to work with large data sets while succinctly summarizing conclusions required