Data Scientist
The Data Scientist at Confidential Client is responsible for building and implementing predictive models to optimize lending decisions and analyze consumer risk behavior. This role involves collaborating with engineering teams, developing technical tools, designing A/B tests, and communicating actionable insights to senior management to drive business growth in a dynamic fintech environment.
About CareerTakes
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👉 Important disclosure: CareerTakes is a third-party recruiting platform supporting this hiring process. If selected, you will be employed directly by our client, Financial Services.
Applicants for this role may also receive access to additional matched opportunities through the CareerTakes platform.
Role summary
Confidential Client is hiring a Data Scientist to build predictive models and analytics that inform credit and underwriting decisions. This role blends statistical modeling, Python development, SQL-based reporting, and cross-functional collaboration to drive measurable improvements in lending outcomes. The position supports flexible remote work with a presence option in the downtown Chicago office.
Key Responsibilities
- Build and validate predictive models to optimize lending and underwriting decisions using statistical and simulation techniques
- Analyze consumer risk behavior and portfolio performance to identify trends and business opportunities
- Work closely with software engineering to implement models into production and perform post-release validation
- Develop tools and standardized workflows to streamline model development and monitoring
- Establish KPIs and build automated reporting dashboards using SQL and Python
- Design and evaluate A/B tests for models and underwriting strategies; translate results into actionable recommendations
- Partner with cross-functional teams (product, engineering, operations) to discover insights and prioritize analyses
- Communicate concise, data-driven strategies and results to senior management
Minimum Qualifications
- Bachelor’s degree in a quantitative discipline (required)
- 2+ years of professional experience in data analytics, applied statistics, or modeling
- Proficiency with SQL and relational databases
- Programming experience in Python for data analysis and modeling
- Strong quantitative, analytical, and problem-solving skills with attention to detail
- Experience collaborating with cross-functional teams in a fast-paced environment
Preferred / Nice-to-Have
- Advanced degree in math, statistics, data science, quantitative finance, engineering, or related field
- Experience in specialty finance, consumer lending, or FinTech
- Background in marketing analytics, experimentation design, or A/B testing frameworks
- Familiarity with model monitoring, MLOps practices, or production deployment of machine learning models
Benefits & Work Environment
- Competitive salary and benefits package
- Flexible remote work with a convenient downtown Chicago office option
- Fast-paced, growth-stage environment with opportunity for immediate impact
- Collaborative and mission-driven team culture
- Equal opportunity employer practices
How to Apply
Applicants will be considered through the CareerTakes platform. If you’re interested, submit your resume and a brief note highlighting relevant modeling projects (Python/SQL) and any experience with lending or FinTech analytics.
Authenticity & Communication Notice
Please verify that any communication about this role comes from official CareerTakes channels or direct messages from Confidential Client via authorized applicant tracking systems. If something appears suspicious, contact CareerTakes support before responding.
Equal Opportunity & Hiring Transparency
CareerTakes and our client are Equal Opportunity Employers committed to building a diverse and inclusive workforce. We prohibit discrimination or harassment of any kind. To support a fair and efficient hiring process, AI tools may be used to assist with application review or resume screening. These tools do not replace human decision-making. Final hiring decisions are made by people.
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