Senior Predictive Liability Analytics Lead

About the role

At Corebridge Financial, we believe action is everything. That's why every day we partner with financial professionals and institutions to make it possible for more people to take action in their financial lives, for today and tomorrow. We align to a set of Values that are the core pillars that define our culture and help bring our brand purpose to life: We are stronger as one: We collaborate across the enterprise, scale what works and act decisively for our customers and partners. We deliver on commitments: We are accountable, empower each other and go above and beyond for our stakeholders. We learn, improve and innovate: We get better each day by challenging the status quo and equipping ourselves for the future. We are inclusive: We embrace different perspectives, enabling our colleagues to make an impact and bring their whole selves to work. Who you'll work with Group: Balance Sheet Risk Management (BSRM) Reports to: Head of Actuarial Strategy & Integration 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.

Responsibilities

• Modeling & Innovation (hands-on)
• 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.
• Integration with actuarial / finance
• 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.
• MLOps, deployment & monitoring
• 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.
• Cross-functional enablement
• 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.

Requirements

• Master's/PhD in Statistics, Data Science/ML, Applied Math, Computer Science, or Actuarial Science; FSA/ASA a plus (or equivalent domain depth).
• Certifications in ML/AI (nice to have).
• 7+ 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: tran

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