Data Scientist, Credit Risk Analytics

Jobtailor; Arizona; On-site; USD 120,000 - 170,000; Full time

Job summary

Jobtailor in the United States (Arizona) seeks a data scientist to build and deploy machine learning models for credit and fraud risk. You will leverage Python and SQL, collaborate with engineering to productionize solutions, and analyze portfolio performance to drive risk strategies.

You will also develop internal tools to improve productivity, monitor models, and contribute to governance and ML ops under regulatory guidelines.

Qualifications

• 2-3+ years in fintech, finance, or similar field applying statistical and machine learning predictive techniques
• Consumer lending experience (unsecured personal loans or credit cards) is a strong plus
• Advanced degree (M.S./Ph.D.) in a technical field preferred
• Expert knowledge of Python
• Expert knowledge of SQL
• Understanding of model governance and ML ops principles

Responsibilities

• Build ML models for credit and fraud risk management and deploy them in production
• Use credit bureau data and customer information at scale to develop risk strategies
• Propose and execute strategic solutions aligned with company objectives
• Analyze portfolio performance and identify trends through root-cause analyses
• Develop internal tools and workflows to improve data science productivity
• Monitor production credit risk models and extract actionable insights
• Assess new ML algorithms and features from alternative data providers
• Support risk management, investor services, operations, and corporate development with ad-hoc analyses
• Shape credit risk strategy and business decisions
• Participate in interviews across recruiter, department, team, and final-round stages

Skills

Machine Learning

Python

SQL

Data Analysis

Model Governance

ML Ops

Credit Risk

Root-Cause Analysis

Education

M.S./Ph.D.

Job description

• Build machine learning models for credit and fraud risk management and collaborate with engineering to deploy them in production
• Use credit bureau reports and customer-supplied information at scale to develop credit and fraud strategies
• Propose and execute strategic solutions to complex business problems aligned with company objectives
• Analyze portfolio performance at granular segment levels, identify trends, conduct root-cause analysis, and communicate recommendations
• Develop internal tools and workflow solutions to improve data science productivity and operational efficiency
• Monitor production credit risk models and strategies and extract actionable insights
• Assess new machine learning algorithms and features from alternative data providers
• Conduct ad-hoc analyses supporting risk management, investor services, operations, and corporate development
• Shape credit risk strategy and business decisions
• Participate in recruiter, department, team/virtual, and final-round interviews

Requirements

• 2-3+ years of work experience in fintech, finance, or another high-impact field applying statistical and machine learning predictive techniques
• Consumer lending experience in unsecured personal loans or credit cards is a strong plus
• Advanced degree (M.S./Ph.D.) preferably in statistics, computer science, engineering, physical sciences, economics, or a related technical field
• Expert knowledge of statistical programming languages such as Python
• Expert knowledge of database languages such as SQL
• Solid understanding of coding best practices, model documentation, and ML ops principles
• Ability to translate complex technical subject matter into clear, actionable business strategies
• Ability to collaborate across engineering, product, and compliance functions
• Ability to work unsupervised in a fast-paced environment and prioritize parallel projects
• Ability to innovate within regulatory guidelines
• Commitment to reproducible research and model governance
• Self-motivated, results-oriented, enthusiastic, and creative approach

Core Competencies

Demonstrates expertise in building and deploying machine learning models for credit and fraud risk management, utilizing statistical programming languages like Python and SQL. Capable of analyzing portfolio performance and translating complex data insights into actionable business strategies while ensuring compliance with regulatory guidelines.

Highest-signal resume keywords

• Machine Learning Model Development
• Statistical Programming (Python)
• Database Management (SQL)
• Credit Risk Strategy
• Consumer Lending Experience

ATS Optimization Keywords

Hard Skills

• Machine Learning
• Statistical Analysis
• Data Analysis
• Root-Cause Analysis
• Model Documentation
• ML Ops Principles
• Predictive Techniques
• Data Science Productivity Tools
• Workflow Solutions
• Credit Bureau Reports

Soft Skills

• Collaboration
• Communication
• Self-Motivation
• Results Orientation
• Creativity

Certifications & Qualifications

• Advanced Degree (M.S./Ph.D.)

Industry Keywords

• Fintech
• Finance
• Credit Risk Management
• Fraud Risk Management
• Consumer Lending
• Unsecured Personal Loans
• Credit Cards
• Regulatory Compliance
• Operational Efficiency
• Business Strategy