Senior Data Scientist of Credit Risk Modeling Location Hybrid remote in West Valley City, UT : At Clicklease, we're on a unique mission - to empower the small business owners often overlooked by traditional lenders. If you're considering joining our team, envision yourself at the heart of our vibrant headquarters in West Valley City, UT, or contributing to our impactful operations from Radial, Alajuela, Costa Rica. At Clicklease, we've cultivated a dynamic work environment that goes beyond routine services. At Clicklease, you'll play a pivotal role in transforming equipment financing into a gateway for entrepreneurs to turn their dreams into reality. If you're passionate about purpose-driven work, innovation, and making a tangible impact, Clicklease is where your career journey begins. Join us in shaping a future where every business owner has the opportunity to thrive. Join our team at Clicklease as a Senior Data Scientist of Credit Risk Modeling, where you'll lead the development and implementation of cutting-edge credit risk models to drive informed decision-making and minimize losses. Utilize your expertise in statistical analysis and machine learning techniques to optimize credit risk strategies across diverse products and services, contributing to the enhancement of our risk management frameworks. If you're a seasoned professional with a passion for innovation and a track record of success in the financial services industry, we want to hear from you! Apply now to be part of a dynamic team shaping the future of credit risk assessment.
Compensation : $130,000 - $170,000 based on experience Modality : Hybrid (3 days in office, 2 days remote) FLSA Exemption : Exempt Schedule : Monday to Friday, 9:00 AM - 5:00 PM
What you'll be doing: Develop, validate, and implement credit risk models that predict customer behavior and assess credit risk across various products and services.
Utilize advanced statistical analysis and machine learning techniques to optimize credit risk strategies, improving decision-making and minimizing losses.
Conduct complex data analyses to identify trends, patterns, and insights related to credit risk, contributing to the enhancement of risk management frameworks.
Collaborate with cross-functional teams, including finance, operations, and technology, to integrate credit risk models into the broader operational infrastructure.
Lead projects to explore new data sources and modeling techniques that can enhance the accuracy and effectiveness of credit risk assessments.
Mentor junior data scientists and analysts, fostering a culture of analytical rigor and innovation within the team.
Stay current with the latest developments in data science, credit modeling, and financial regulations, ensuring that models comply with both internal standards and external regulatory requirements.
Design and implement testing frameworks to continuously monitor model performance and accuracy, making adjustments as necessary to maintain high standards of reliability and validity.
What you have: Bachelor's or Master's degree in Statistics, Mathematics, Economics, Computer Science, or a related quantitative field.
5+ years of experience in data science or analytics, with a strong focus on credit risk modeling in the financial services industry.
Expertise in statistical modeling techniques, such as logistic regression, decision trees, and machine learning algorithms.
Proficiency in programming languages and tools used for data analysis and model development (e.g., Python, R, SQL).
Demonstrated ability to handle large datasets and conduct complex analyses using big data technologies and platforms.
Strong problem-solving skills, with a track record of developing innovative solutions to analytical challenges.
Excellent communication skills, with the ability to present complex analytical findings clearly and effectively to non-technical stakeholders.
What will make you stand out: PhD in a quantitative discipline.
Experience with advanced machine learning and artificial intelligence techniques, such as neural networks, natural language processing, or reinforcement learning.
Publications in relevant academic journals or presentations at industry conferences.
Leadership experience or significant contributions to major projects in credit risk modeling.
