Director of Data Science- Credit Risk Scoring

Company Description

Experian is a global data and technology company, powering opportunities for people and businesses around the world. We help to redefine lending practices, uncover and prevent fraud, simplify healthcare, create marketing solutions, and gain deeper insights into the automotive market, all using our unique combination of data, analytics and software. We also assist millions of people to realize their financial goals and help them save time and money.

We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more industry segments.

We invest in people and new advanced technologies to unlock the power of data. As a FTSE 100 Index company listed on the London Stock Exchange (EXPN), we have a team of 22,500 people across 32 countries. Our corporate headquarters are in Dublin, Ireland. Learn more at experianplc.com.

Job Description

Job description

We are looking for an experienced and experienced Director of Data Science to lead our data science projects with a focus on credit risk scoring. We are looking for expertise in building, implementing, and optimizing credit risk models, including machine learning (ML) models, and the ability to manage a team of data scientists.

You are a hands-on coder with experience using data-driven insights to lead decision-making. You have a background in modeling and experience of Python, with demonstrated experience delivering solutions to complex credit risk challenges. As a Director, Data Science you will be reporting to the VP, Analytics Products Build.

Responsibilities:

• Build, and mentor a team of data scientists specializing in credit risk modeling.
• Build the data science strategy for credit risk modeling and analytics with our goals.
• Collaborate with teams, including product, engineering, and compliance, to integrate models into our workflows.
• Hands-On Development:
• Design scalable, accurate, and explainable credit risk models and models solving problems across the entire credit life-cycle using machine learning and traditional statistical techniques.
• Write high-quality, production-grade Python code to prototype and implement models.
• Ensure comply with regulatory requirements and company policies.
• Analyze large datasets to identify trends and drivers of credit risk, ensuring applicable insights for partners.
• Develop approaches to feature engineering and data enrichment to improve model performance.
• Maintain existing models, ensuring they remain up-to-date with changing data and our needs.
• Communicate technical concepts and model outcomes to non-technical partners.
• Provide strategic insights to executive leadership based on data science outcomes.

Qualifications:

• Master's degree in Data Science, Statistics, Computer Science, Mathematics, or a related field.
• 8+ years of experience in data science, with a focus on credit risk modeling.
• Experience leading teams.
• Hands-on experience developing and deploying machine learning models, especially in credit risk contexts.
• Fundamental knowledge in general processes around targeting, Fraud detection, acquisitions, Account management, collections
• Technical Expertise:
• Advanced proficiency in Python and main libraries such as Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch.
• Knowledge of statistical modeling, feature engineering, and machine learning algorithms.
• Experience working with big data technologies and distributed systems (e.g., Spark, Hadoop).
• Knowledge of credit risk scoring methodologies, regulatory frameworks, and model governance.
• Experience scrapping data and parsing unstructured data

Qualifications:

• Capabilities with the ability to inspire and mentor team members.
• Translate complex technical details into applicable insights for diverse partners.
• Thinker with a creative approach to the ability to develop unique solutions
• Experience with Visualization tools such as Tableau

Benefits

• Health, Dental, Vision Insurance
• 401k match up to 4% of 100% of your salary
• 20% app bonus target
• Remote work environment #li-remote

Qualifications

Qualifications

• Education: Master's degree in Data Science, Statistics, Computer Science, Mathematics, or a related field.
• 8+ years of experience in data science, with a focus on credit risk modeling.
• Hands-on experience developing and deploying machine learning models, especially in credit risk contexts.
• Advanced proficiency in Python and main libraries such as Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch, etc. Deep knowledge of statistical modeling, feature engineering, and machine learning algorithms.
• Experience working with big data technologies and distributed systems is a plus (e.g., Spark, Hadoop). Strong knowledge of credit risk scoring methodologies, regulatory frameworks, and model governance. Extensive experience with scrapping data and parsing unstructured data

Additional Information

Our uniqueness is that we celebrate yours. Experian's culture and people are important differentiators. We take our people agenda very seriously and focus on what matters; DEI, work/life balance, development, authenticity, collaboration, wellness, reward & recognition, volunteering... the list goes on. Experian's people first approach is award-winning; World's Best Workplaces™ 2024 (Fortune Top 25), Great Place To Work™ in 24 countries, and Glassdoor Best Places to Work 2024 to name a few. Check out Experian Life on social or our Careers Site to understand why.

Experian is proud to be an Equal Opportunity and Affirmative Action employer. Innovation is an important part of Experian's DNA and practices, and our diverse workforce drives our success. Everyone can succeed at Experian and bring their whole self to work, irrespective of their gender, ethnicity, religion, colour, sexuality, physical ability or age. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.