Analyst, Data Operations, Ares Insurance Solutions (AIS)

Ares Management Corporation•New York, NY

1d•$95,000 - $110,000•Onsite

About The Position

Ares Insurance Solutions (AIS) is seeking an Analyst to support the Asset Analytics and Data Operations function responsible for investment portfolios backing life and annuity insurance liabilities. This role will focus on maintaining the quality, accuracy, and completeness of investment data used across investment management, actuarial, finance, and forecasting processes. The Analyst will play a key role in identifying, investigating, and resolving data anomalies, reconciliation exceptions, and data discrepancies across multiple investment systems and data sources, helping ensure the integrity and reliability of investment data used across the organization. Working closely with Asset Analytics, Investment Operations, Finance, Accounting, and Technology teams, the Analyst will support critical data governance and operational processes. The individual will also assist with the preparation and validation of asset-related inputs for FP&A and projection models and may support the execution of forecasting tools and processes. This role is well-suited for a detail-oriented professional interested in investment operations, insurance asset management, data governance, and cross-functional problem-solving.

Requirements

Bachelor's degree in Finance, Accounting, Economics, Mathematics, Statistics, Data Analytics, Information Systems, Engineering, Actuarial Science, or a related field.
1–4 years of experience in investment operations, data management, accounting, financial services, insurance, asset management, or a related field.
Experience working with financial data, reconciliations, operational controls, or data quality processes preferred.
Exposure to life insurance, annuities, investment accounting, investment operations, or asset management is a plus.
Advanced proficiency in Microsoft Excel.
Working knowledge of SQL and database querying concepts.
Ability to analyze large datasets and investigate data discrepancies across multiple systems.
Strong analytical and problem-solving skills.
Exceptional attention to detail and commitment to data accuracy.
Ability to independently investigate complex data anomalies and determine root causes.
Strong organizational skills and ability to manage multiple priorities simultaneously.
Effective communication and stakeholder management skills.
Ability to work collaboratively across business, operations, finance, actuarial, and technology teams.
Continuous improvement mindset with a focus on operational excellence and control effectiveness.

Nice To Haves

Experience with Python, Power BI, Alteryx, or other data analysis and automation tools is a plus.
Familiarity with investment accounting concepts and investment data structures is preferred.
Familiarity with fixed income, structured products, private credit, or insurance investment portfolios.
Understanding of investment accounting platforms, custodial data, security master data, and investment operations workflows.
Experience supporting financial projections, planning processes, or actuarial and finance data requirements.
Progress toward a professional designation in accounting, actuarial science, finance, or a related field is a plus, but not required.

Responsibilities

Monitor the quality, accuracy, and completeness of investment data across various asset classes, including public fixed income, structured products, private credit, mortgages, and alternative investments.
Perform daily, monthly, and quarter-end reconciliations across investment accounting systems, portfolio management platforms, custodians, data warehouses, and analytical applications.
Investigate and resolve reconciliation breaks involving positions, market values, book values, cash balances, accrued interest, investment income, ratings, security master attributes, and cash flows.
Analyze data anomalies and reconciliation exceptions across multiple sources and determine root causes, whether related to source feeds, accounting treatment, data transformations, system configurations, timing differences, or operational processes.
Partner with Investment Operations, Accounting, Technology, and external service providers to investigate and resolve data anomalies and reconciliation exceptions and validate remediation efforts.
Maintain reconciliation inventories, exception logs, and support documentation to ensure transparency and auditability of the resolution process.
Ensure data consistency across upstream and downstream systems supporting investment management, actuarial modeling, FP&A, regulatory reporting, and management reporting.
Assist in establishing and enhancing data quality controls, exception monitoring, and operational workflows to proactively identify anomalies before they impact business processes.
Support data validation efforts related to new security onboarding, portfolio transfers, acquisitions, system implementations, and data migration projects.
Serve as a key liaison between Asset Analytics, Investment Operations, Finance, Accounting, Actuarial, and Technology teams.
Communicate data anomalies, reconciliation findings, business requirements, and remediation priorities to stakeholders across functions.
Coordinate exception resolution efforts from identification through testing, validation, and closure.
Participate in user acceptance testing (UAT) for system enhancements, data feeds, platform upgrades, and process changes.
Maintain documentation of data definitions, business requirements, reconciliation procedures, exception management workflows, and operating controls.
Prepare, validate, and reconcile asset-related inputs used in FP&A forecasts, planning exercises, and financial projection models.
Ensure completeness and accuracy of portfolio attributes, asset cash flows, yields, spreads, ratings, durations, reinvestment assumptions, and other modeling inputs.
Support periodic forecasting and planning processes by providing timely and accurate data extracts and validation checks.
Assist in executing asset-side projection tools and perform reasonability reviews of outputs.
Investigate and resolve data anomalies, exceptions, or inconsistencies that may impact model inputs, assumptions, or projection results.
Identify opportunities to improve reconciliation processes, data controls, and operational efficiency.
Support initiatives to strengthen data governance and improve data lineage, transparency, and consistency across systems.
Assist with the development of automated data validation and reconciliation processes where appropriate.
Contribute to the establishment of best practices for investment data management and operational controls.