Explore Jobs and Careers
Welcome to bluCognition – where innovation meets expertise in data and risk management. At bluCognition, we believe in harnessing the power of data to navigate the complexities of risk, empowering our clients to make informed decisions that drive their success. bluCognition is an equal opportunity employer and celebrates diversity to ensure an inclusive environment for all employees. We are a dynamic and forward-thinking team and always on the lookout for talented individuals to join us on this exciting journey. Trust us to find the right opportunity for you.
Open Positions
Technology
Req ID - bluC/2026/0085Analyst - Data Science
Shift - Day/Fixed
Location - Remote
We are looking for an analytically curious and detail-oriented professional with up to 4 years of experience in data science and credit risk. This is an excellent opportunity to deepen your expertise in credit risk analytics
We are looking for an analytically curious and detail-oriented professional with up to 4 years of experience in data science and credit risk. This is an excellent opportunity to deepen your expertise in credit risk analytics and machine learning within a fast-growing AI/ML firm.
The ideal candidate is proficient in Python, SQL and data analysis libraries, comfortable working with large datasets, and has hands-on exposure to credit risk or ML modelling concepts. You will work alongside experienced data scientists and risk professionals, contributing to ad-hoc analyses, model validation, and bureau data exploration — while building the skills to grow into more independent modelling responsibilities over time.
- 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.
- Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
- Hands-on proficiency in Python — particularly pandas, NumPy, and data analysis libraries — for data manipulation and exploratory analysis.
- Working knowledge of SQL for data extraction and querying large datasets.
- Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
- Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
- Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.
- Familiarity with data visualization tools such as Power BI for presenting analytical insights.
- Basic awareness of cloud-based data platforms (AWS) and big data tools.
- Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.
- Degree from a top-tier institution; particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.
- Work on end-to-end credit portfolio analytics driving real financial outcomes.
- Exposure to U.S. credit card portfolios and data-driven risk strategy.
- Collaborate with cross-functional teams across risk, marketing, and product to shape growth and profitability.
Technology
Req ID - bluC/2025/0060Analyst - Credit & Fraud Risk
Shift - Day/Fixed
Location - Remote
We are looking for analytically strong professionals with deep expertise in credit card analytics, portfolio management, and risk strategy—particularly those who have worked on LTV (Lifetime Value) and Risk– Reward
We are looking for analytically strong professionals with deep expertise in credit card analytics, portfolio management, and risk strategy—particularly those who have worked on LTV (Lifetime Value) and Risk– Reward trade-off modelling.
The ideal candidate brings experience working with U.S. credit card portfolios and can connect data- driven insights to business profitability and portfolio health.
- Develop, validate, and enhance LTV models, credit risk models, and portfolio profitability frameworks.
- Work on Risk-Reward trade-off modelling to optimize acquisition and portfolio strategies—balancing growth and default risk.
- Extract and explore data, validate data integrity, perform ad hoc analysis, evaluate new data sources for usage in strategy development.
- Lead portfolio analytics initiatives to track and improve credit performance.
- Partner with risk, product, and marketing teams to design data-driven credit policies, limit strategies, and customer lifecycle interventions.
- Conduct profitability segmentation to identify high-value and high-risk customer cohorts.
- Support pricing and credit limit optimization using predictive modelling and scenario simulations.
- Analyse large-scale datasets (internal + bureau data) to extract actionable insights for portfolio health monitoring.
- 2 years + of experience in credit card, consumer lending, or portfolio risk space.
- Proven experience in LTV modelling, risk-reward trade-off, and portfolio profitability optimization.
- Hands-on proficiency in Python or R for statistical modelling and SQL for data extraction/manipulation.
- Expertise in predictive modelling (logistic regression, decision trees, gradient boosting, survival analysis).
- Experience with credit bureau data, scorecards, and portfolio forecasting techniques and familiarity with U.S. credit systems.
- Ability to communicate findings clearly to both technical and business audiences.
- Familiarity with cloud-based data platforms (AWS, GCP, Azure) and big data tools (Spark, Databricks).
- Proficiency with data visualization tools such as Tableau, Power BI, or Looker.
- Degree from a top-tier institution; particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.
- Work on end-to-end credit portfolio analytics driving real financial outcomes.
- Exposure to U.S. credit card portfolios and data-driven risk strategy.
- Collaborate with cross-functional teams across risk, marketing, and product to shape growth and profitability.
Diversity, Equity and Inclusion at bluCognition
At bluCognition, Diversity, Equity, and Inclusion (DE &I) are not just buzzwords – they are fundamental principles that shape our identity, drive innovation and contribute to the success and well-being of our entire workforce. We are committed to cultivating a robust culture of DE&I that permeates every aspect of our operations. We recognize that embracing diversity goes beyond mere representation; it is about creating an environment where every individual feels valued, included, and empowered to contribute their unique perspectives.