Assistant Vice President, Strategic Asset Allocation Analytics

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Alternate Locations: Radnor, PA (Pennsylvania)

Work Arrangement:

Hybrid :
Employee will work 3 days a week in a Lincoln office

Relocation assistance:is not available for this opportunity.

Requisition #:76653

• Engineer reliable Python services and SQL backed data pipelines that ingest asset, liability, and market data; orchestrate runs; and expose results to strategy and risk partners (APIs, notebooks, dashboards).
• Productionize research prototypes into hardened components (configuration management, unit/integration tests, logging, lineage, and automated documentation) supporting enterprise wide SAA across U.S. and Bermuda entities.

• Implement and scale optimization, scenario analysis, and stress testing tools used to construct SAA under capital, rating, liquidity, and ALM constraints; parameterize for both U.S. RBC and Bermuda BSCR frameworks.
• Integrate cash flow projections (e.g., Bond Edge outputs) and reconcile them to portfolio views with auditable data controls.

• Model and map security master and position data to NAIC designations and statutory schedules and to BMA BSCR asset categories and SBA portfolios; embed validation checks aligned to reporting rules.

• Work across the Investments team to partner with insurance business units, ALM, Actuarial, Enterprise Risk Management, and Finance.

• Fluent in multiple languages with depth in Python and SQL (required). You also bring working proficiency in other languages for legacy model integration.
• Hands on experience building scalable, object-oriented solutions/components for model enhancements/extensions across different products/assumptions. Enhancing existing core framework to scale model with change requests from product owners (annuities, life, group etc.) while maintaining code readability, documentation, and audit trail.
• Hands on data engineering: relational modeling, performance tuning, data integration (with tools like Bloomberg & Intex), orchestration, version control, testing frameworks, and observability.
• Quant/optimization toolkit experience (e.g., pandas, Num Py, Sci Py, Pyomo/OR Tools) and time series/scenario modeling.
• Code testing/automation using CI/CD framework, Backtesting of strategy – creating backtesting data and summarizing model results and reporting Investment analytics by potentially leveraging Lincoln’s AI tools/suite of products.
• Experience in producing asset cashflows utilizing tools like Factset/Bloomberg/Intex is a plus.