Senior Predictive Liability Analytics Lead

Corebridge Financial • Los Angeles, CA

2d • Hybrid

About The Position

Join BSRM as our hands-on Senior Predictive Liability Analytics Lead. You’ll build next-gen short-term models of policyholder behavior starting with annuity surrenders, withdrawals, and utilization. You’ll collaborate with other stakeholders such as financial planning, ALM, pricing, valuation and capital as appropriate. This is a technical leadership role (not initially a people-manager or strategy role). You’ll do the math, write the code, build models, and mentor by example.

Requirements

• Master’s/PhD in Statistics, Data Science/ML, Applied Math, Computer Science, or Actuarial Science; FSA/ASA a plus (or equivalent domain depth).
• 7–10+ years building production predictive models; insurance/annuity or long-duration liability exposure preferred.
• Practical wins in behavior modeling (surrender/utilization/lapse) and integration
• Comfortable spanning structured + unstructured data and bridging to projection engines.
• Clear, concise communicator; strong documentation habits; bias to ship and iterate.
• Mentors by example; sets standards for code quality, reproducibility, and testing.
• Balances accuracy, interpretability, and operational simplicity under governance.
• Collaborate within a highly matrixed organization.
• Python (pandas, NumPy, scikit-learn, XGBoost/LightGBM, PyTorch/TensorFlow), SQL
• R
• NLP/LLM: transformers/embeddings, RAG, prompt engineering; genetic/evolutionary search for features/hyper-params.
• Databricks/Spark, Snowflake, AWS/Azure; MLflow, model registries, CI/CD; Tableau/Power BI for monitoring & storytelling.
• Working familiarity with Actuarial platform (AXIS/Prophet/RAFM/etc.) integration patterns (assumption tables, mapping layers).

Nice To Haves

• Certifications in ML/AI (nice to have).

Responsibilities

• Design predictive and semi-structural models with a short-term focus (performance focused on fitting the next 18-36 months) on long-horizon behavior (surrenders, partial withdrawals, lapse, rider utilization) using GLMs/GAMs, survival & hazard models (Cox, discrete-time, competing risks), tree ensembles (Boost/LightGBM/CatBoost), and deep learning choosing the most appropriate tool depending on the business problem.
• Leverage unstructured data (contract text, correspondence, customer relationship notes, call transcripts) via NLP/transformer embeddings, RAG pipelines, and LLM-assisted document parsing to create novel behavioral features within guardrails.
• Pilot generative-AI (foundation models) for feature extraction/summarization; use genetic/evolutionary algorithms for feature selection, architecture search, or synthetic cohort generation when appropriate.
• Make models scenario-aware: incorporate drivers like credited rate, market rate spreads, moneyness, surrender charge state, distribution channel effects; calibrate elasticity to economic conditions documented in industry studies.
• Translate model outputs into curves/driver functions consumable by projection engines (e.g., Moody’s AXIS, Aon Pathwise, Prophet, RAFM, or internal models); generate reproducible, versioned results tables.
• Share models with valuation/projection/ALM teams so behavior sensitivities can be considered alongside assumptions that normally flow through cash-flow projections, LDTI assumption updates, RBC/CTE stresses, and hedge effectiveness studies.
• Partner with valuation and pricing to reconcile actual vs. expected and attribute earnings/variance to behavior; document the “model story” and explainability for governance.
• Build training/scoring pipelines in Python/SQL on Databricks/Spark/Snowflake/AWS; track experiments with MLflow/DVC, version in Git, package with containers, and serve via batch/API.
• Stand up dashboards for calibration, drift, stability, and bias; set retraining schedules, fallback models, rollback criteria, and automated alerts.
• Co-design experiments (A/B, uplift, causal inference) with Business Owners/Operations/Distribution to test interventions; when warranted, explore contextual bandits/RL for offer timing and messaging.
• Support Actuarial/Finance/Capital on scenario stress, attribution, and sensitivity runs; including helping to present results to model governance, assumption committees, and internal validation as needed.

Benefits

• Health and Wellness: We offer a range of medical, dental and vision insurance plans, as well as mental health support and wellness initiatives to promote overall well-being.
• Retirement Savings: We offer retirement benefits options, which vary by location. In the U.S., our competitive 401(k) Plan offers a generous dollar-for-dollar Company matching contribution of up to 6% of eligible pay and a Company contribution equal to 3% of eligible pay (subject to annual IRS limits and Plan terms). These Company contributions vest immediately.
• Employee Assistance Program: Confidential counseling services and resources are available to all employees.
• Matching charitable donations: Corebridge matches donations to tax-exempt organizations 1:1, up to $5,000.
• Volunteer Time Off: Employees may use up to 16 volunteer hours annually to support activities that enhance and serve communities where employees live and work.
• Paid Time Off: Eligible employees start off with at least 24 Paid Time Off (PTO) days so they can take time off for themselves and their families when they need it.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level