Senior Pricing Analyst – Modeling, Machine Learning

YOUSE

. Develop, update, validate, and monitor statistical and Machine Learning models applied to Pricing and insurance .

Posted 9/19/2026full-timeSão Paulo • BrazilSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and validating statistical and Machine Learning models for Pricing and insurance, with a strong focus on risk management, profitability, and customer behavior analysis. Proficient in translating quantitative insights into actionable business recommendations while collaborating with cross-functional teams.

Highest-signal resume keywords

Statistical ModelingMachine LearningPricing OptimizationRisk ManagementData Analysis

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Statistical ModelsGLMsPythonRSQLModel ValidationBacktestingCohort SimulationsPortfolio SimulationsPricing Methodologies

Soft Skills

Strong CommunicationPresentation Skills

Tools & Technologies

Power BILookerTableauDatabricksGuidewireEmblemRadarSAS RTDMAWS

Industry Keywords

InsurancePricingRisk SegmentationChurnRetentionLoss RatioProfitabilityDemand ElasticityCustomer BehaviorSegmentation

Tech Stack

Tools & technologies

AWSGuidewirePythonSQLTableau

About the role

Key responsibilities & impact

Develop, update, validate, and monitor statistical and Machine Learning models applied to Pricing and insurance
Build and enhance claims frequency and severity models, including GLMs and other statistical and Machine Learning techniques
Develop price and demand elasticity models and analyses, assessing price, conversion, retention, and customer behavior
Develop renewal propensity, churn, and retention models
Analyze customer and risk segmentation
Support pricing optimization studies, balancing risk, profitability, competitiveness, conversion, and retention
Develop models and analyses for Auto, Life, and Home Insurance
Perform cohort and portfolio simulations
Analyze observed versus estimated loss ratios and identify recalibration opportunities
Simulate the impact of changes to rates, underwriting rules, segmentation, and pricing strategies
Conduct backtesting, validation, and model performance analyses
Support the definition of Pricing assumptions, variables, segmentation, and methodologies
Identify risk and profitability drivers and translate statistical results into business recommendations
Design KPIs to track the performance of models and strategies in production
Work with Data and Technology teams on the implementation, deployment, and monitoring of models in production
Prepare analyses and presentations for technical and executive forums
Contribute to the advancement of Modeling and Machine Learning methodologies, tools, and processes applied to Pricing

Requirements

What you’ll need

Degree in Statistics, Actuarial Science, Mathematics, Data Science, Engineering, Economics, or a related field
Professional experience in Pricing, Modeling, Data Science, Actuarial Science, Risk, or analytical functions within the insurance industry
Hands-on experience with statistical modeling and/or Machine Learning
Knowledge of statistical models applied to insurance, including frequency and severity GLMs, frequency × severity, price and demand elasticity, renewal propensity and churn, retention, segmentation, and pricing optimization
Knowledge of Pricing and risk management for Auto, Life, and Home Insurance
Knowledge of loss ratio, premium, exposure, frequency, severity, profitability, and margin metrics and methodologies
Knowledge of model validation, calibration, backtesting, and monitoring
Knowledge of portfolio and cohort simulations
Ability to assess trade-offs among risk, price, conversion, retention, and profitability
Knowledge of Python or R
Knowledge of SQL
Knowledge of Power BI, Looker, Tableau, or equivalent tools
Ability to translate quantitative analyses into business recommendations
Strong communication and presentation skills for technical and non-technical audiences
Experience with or knowledge of Databricks, Guidewire, Emblem, Radar, SAS RTDM, or AWS is desirable
Knowledge of data architectures, modeling pipelines, and production implementation and monitoring processes is an advantage
Additional advantages include experience in Pricing for Auto, Life, and Home Insurance; price elasticity and optimization; churn, renewal, and retention; cohort and portfolio simulations; model risk management; rate setting; risk segmentation; portfolio optimization; medium-sized or large insurers; and tools such as GUIDE, Emblem, Radar, and SAS RTDM

Benefits

Comp & perks

Hybrid work model: 2 days on-site and 3 days remote
7.5-hour workday
Employment under the CLT labor regime