Demonstrates expertise in leading credit-risk and financial-decision modeling initiatives, utilizing statistical modeling and machine learning techniques to drive impactful outcomes. Proficient in collaborating with technical teams to deploy models and communicate findings effectively to diverse stakeholders.

Highest-signal resume keywords

Statistical ModelingSupervised LearningPython ProgrammingSQL ProficiencyModel Evaluation

Tech Stack

Tools & technologies

Amazon RedshiftBigQueryKafkaPythonScikit-LearnSparkSQL

About the role

Key responsibilities & impact

Lead credit-risk and financial-decision modeling initiatives from opportunity identification and problem framing through model development, evaluation, and impact assessment
Own the scientific approach, including decision definition, expected value, baselines, validation, and recommendations
Develop and evaluate models and features using large, varied datasets
Set evaluation standards, challenge assumptions, detect leakage and selection bias, account for delayed outcomes, and use time-aware tests and calibration
Estimate effects on revenue, credit losses, exposure, portfolio quality, and customer outcomes
Guide models into production with data and machine learning engineers
Define model and feature requirements, validate deployed behavior, monitor performance and outcomes, and recommend evidence-based changes
Communicate findings and trade-offs to technical and non-technical stakeholders
Lead technical discussions, review modeling approaches, mentor data scientists, and establish reusable practices

Requirements

What you’ll need

Bachelor’s Degree in a quantitative field such as Computer Science, Mathematics, Statistics, Operations Research, Machine Learning or equivalent
Substantial professional experience in data science or applied machine learning, typically six years or more
Evidence of leading consequential modeling work from problem definition through production use and impact assessment
Strong knowledge of statistical modeling, supervised learning, feature engineering, and model interpretation
Practical experience with validation and experimentation, including time-aware evaluation, calibration, bias or selection effects, and post-deployment monitoring
Ability to work independently with large, imperfect datasets and distinguish useful signals from leakage or artifacts
Ability to connect model outputs with financial and customer outcomes and explain decision or policy trade-offs
Ability to form recommendations when evidence is incomplete and state limitations
Strong hands-on Python and SQL skills
Experience with statistical and machine learning libraries such as scikit-learn or XGBoost
Experience with large-scale data platforms or distributed data processing
Experience building reproducible analysis and model workflows
Experience collaborating with engineering teams on deployment and evaluating models after release
Experience building point-in-time features and production model workflows
Advanced degree is a plus
Experience in lending, banking, payments, financial decision domains, credit risk, portfolio economics, causal inference, Databricks, Spark, BigQuery, Amazon Redshift, Kafka, or Amazon Kinesis is a plus

Benefits

Comp & perks

Annual leave (up to 14 days in the first year, granted proportionally according to the month of employment; can be used from the date of hire)
Personal leave (5 days each year, granted proportionally according to the month of employment)
PayPay special paid leave for illnesses, injuries, hospital visits, and care of employees, family members, or pets
Social Insurance (health insurance, employee pension, employment insurance and compensation insurance)
401K
Translation/Interpretation support
VISA sponsor + Relocation support
Super Flex Time (No Core Time)
Late overtime allowance
Annual salary reviewed once a year
Payroll payment option via digital salary payment “PayPay Paycheck”

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ScienceApplied Machine LearningFeature EngineeringModel InterpretationValidation and ExperimentationTime-Aware EvaluationCausal InferenceReproducible AnalysisModel WorkflowsData Analysis

Soft Skills

Independent WorkCommunicationMentoringProblem-SolvingDecision-Making

Tools & Technologies

Scikit-LearnXGBoostDatabricksSparkBigQueryAmazon RedshiftKafkaAmazon Kinesis

Industry Keywords

Credit RiskFinancial Decision DomainsPortfolio EconomicsLendingBankingPayments