Senior Predictive Liability Analytics Lead

AIG

Senior Predictive Liability Analytics Lead at Corebridge Financial collaborating to build predictive models for policyholder behavior. Involves modeling, coding, mentoring and cross-functional collaboration.

Posted 6/24/2026full-timeWoodland Hills • California, New York • 🇺🇸 United StatesSenior💰 $190,000 - $210,000 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureNumpyPandasPythonPyTorchScikit-LearnSparkSQLTableauTensorflow

About the role

Key responsibilities & impact

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.
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.
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.

Requirements

What you’ll need

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: 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).

Benefits

Comp & perks

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.

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Hard Skills & Tools

predictive modelingGLMsGAMssurvival modelshazard modelstree ensemblesdeep learningNLPfeature extractionbehavior modeling

Soft Skills

clear communicationstrong documentationmentoringcode qualityreproducibilitytestingcollaborationiterative mindsetoperational simplicitybias to ship

Certifications

Master’s in StatisticsPhD in Data ScienceFSAASAML certificationAI certification