Job Description & How to Apply Below
blu Cognition is an AI-powered commercial credit intelligence company serving lenders and financial institutions. Our platform combines commercial credit data contributed by 150+ lenders with AI-driven analytics, banking data and fraud detection to help our clients make faster, better credit decisions. Our product suite includes blu Sense, which converts bank statements into standardized, categorized transaction data with real-time financial insights, and Fraud Lens, which detects document tampering and first-party fraud.
Alongside our data products, we run a managed services business supporting underwriting, fraud review, and KYC/KYB and sanctions screening operations for our clients, with teams across North America, Asia and Europe.
About Role
We are looking for an analytically curious, detail-oriented professional with up to 4 years of experience in data science and credit risk, proficient in Python, SQL, and data analysis libraries, comfortable working with large datasets, with hands-on exposure to credit risk or ML modelling concepts. You will work alongside experienced data scientists and risk professionals on ad-hoc analyses, model validation, and bureau data exploration — including U.S. credit card portfolios — while building the skills to grow into more independent modelling responsibilities over time.
Key Responsibilities:
Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
Build foundational knowledge of machine learning techniques and their applications in credit risk, including classification, regression, and ensemble methods.
Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations.
Required
Skills & Qualifications
Must-Have: 2–4 years in data science or credit risk analytics; proficiency in Python (pandas, Num Py) and SQL; foundational ML knowledge (logistic regression, decision trees, gradient boosting); strong communication skills.
Strong Advantage:
Exposure to credit bureau data or consumer lending data; academic/project experience with credit scoring or risk modelling.
Tools (if applicable):
Python (pandas, Num Py), SQL, Power BI, and AWS / cloud-based data platforms.
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