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• b

  Analyst - Credit Underwriting  

  bluCognition

  Permanent - Not Specified

  Posted: 11/05/2026

  Apply Now

  Position: Credit UnderwritingSummary:As a Credit Underwriting & Operat... Read More

  Position: Credit Underwriting

  
  

  Summary:

  As a Credit Underwriting & Operations Analyst, you will be working within the Risk team to assist in the day-to-day manual analysis of new customers on the platform. You should be comfortable with high level financial analysis and data reconciliation in a fast-paced environment with supervision from senior risk members.

  
  

  Roles & Responsibilities:

  
  

  Perform customer-level credit underwriting decisions across multiple B2B products, which include corporate spend cards, EOR, COR, payroll, and PEO, ensuring adherence to established credit policies, procedures, and risk frameworks reviews using both external and proprietary data sources.Utilize and follow a defined set of standard operating procedures alongside Rippling's internal credit tooling and systems to ensure minimum approval criteria is met for an evaluation.Action daily applications pended into the assigned queues. Review the information elements on these cases thoroughly and decide them from approval/decline/follow-up perspective as per procedures.Drive improved performance against established KPIs to achieve functional objectives and ensure effective implementation of the client's risk policies and procedures across all relevant products.Collaborate with the internal sales team to verify if any outstanding documents or information are required from the customer to facilitate approval, ensuring a seamless and efficient process.

  
  

  Requirements:

  
  

  Minimum 1-2 years of experience in credit risk, preferably in SMB/MM commercial lending, including decisioning of new credit product applications.Strong expertise in financial statement analysis, including interpretation of financial and bank statements.Solid understanding of credit risk underwriting and commercial credit products.Experience in credit risk operations and application decisioning within digital/mobile-based environments is highly preferred.Ability to manage multiple tasks, prioritize effectively, and work with strong attention to detail in a dynamic environment.Demonstrated decision-making ability with high accuracy and confidence.Willingness to work in flexible shifts, including PST hours.

  
  

  Educational Qualifications

  Masters in Finance or Masters in Business Administration is highly desirable. Bachelor's degree in Commerce, Finance, Accounting, or a related field. Read Less

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• b

  Analyst - Data Science  

  bluCognition

  Permanent - Not Specified

  Posted: 11/05/2026

  Apply Now

  Key ResponsibilitiesPerform ad-hoc analysis on large-scale data to ext... Read More

  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 & Experience

  
  

  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.

  
  

  Preferred / Additional Skills

  
  

  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.

  
  

  Educational Qualifications

  Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

  Read Less

  Apply Now
• b

  Analyst - Data Science  

  bluCognition

  Permanent - Mumbai

  Posted: 11/05/2026

  Apply Now

  Key ResponsibilitiesPerform ad-hoc analysis on large-scale data to ext... Read More

  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 & Experience

  
  

  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.

  
  

  Preferred / Additional Skills

  
  

  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.

  
  

  Educational Qualifications

  Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

  Read Less

  Apply Now
• b

  Analyst - Data Science  

  bluCognition

  Permanent - Bangalore

  Posted: 11/05/2026

  Apply Now

  Key ResponsibilitiesPerform ad-hoc analysis on large-scale data to ext... Read More

  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 & Experience

  
  

  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.

  
  

  Preferred / Additional Skills

  
  

  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.

  
  

  Educational Qualifications

  Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

  Read Less

  Apply Now
• b

  Analyst - Data Science  

  bluCognition

  Permanent - Ghaziabad

  Posted: 11/05/2026

  Apply Now

  Key ResponsibilitiesPerform ad-hoc analysis on large-scale data to ext... Read More

  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 & Experience

  
  

  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.

  
  

  Preferred / Additional Skills

  
  

  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.

  
  

  Educational Qualifications

  Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

  Read Less

  Apply Now
• b

  Analyst - Data Science  

  bluCognition

  Permanent - Lucknow

  Posted: 11/05/2026

  Apply Now

  Key ResponsibilitiesPerform ad-hoc analysis on large-scale data to ext... Read More

  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 & Experience

  
  

  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.

  
  

  Preferred / Additional Skills

  
  

  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.

  
  

  Educational Qualifications

  Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

  Read Less

  Apply Now
• b

  Analyst - Data Science  

  bluCognition

  Permanent - Shimoga

  Posted: 11/05/2026

  Apply Now

  Key ResponsibilitiesPerform ad-hoc analysis on large-scale data to ext... Read More

  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 & Experience

  
  

  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.

  
  

  Preferred / Additional Skills

  
  

  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.

  
  

  Educational Qualifications

  Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

  Read Less

  Apply Now
• b

  Analyst - Data Science  

  bluCognition

  Permanent - Not Specified

  Posted: 11/05/2026

  Apply Now

  Key ResponsibilitiesPerform ad-hoc analysis on large-scale data to ext... Read More

  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 & Experience

  
  

  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.

  
  

  Preferred / Additional Skills

  
  

  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.

  
  

  Educational Qualifications

  Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

  Read Less

  Apply Now
• b

  Analyst - Data Science  

  bluCognition

  Permanent - Amravati

  Posted: 11/05/2026

  Apply Now

  Key ResponsibilitiesPerform ad-hoc analysis on large-scale data to ext... Read More

  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 & Experience

  
  

  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.

  
  

  Preferred / Additional Skills

  
  

  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.

  
  

  Educational Qualifications

  Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

  Read Less

  Apply Now
• b

  Analyst - Data Science  

  bluCognition

  Permanent - Thiruvananthapuram

  Posted: 11/05/2026

  Apply Now

  Key ResponsibilitiesPerform ad-hoc analysis on large-scale data to ext... Read More

  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 & Experience

  
  

  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.

  
  

  Preferred / Additional Skills

  
  

  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.

  
  

  Educational Qualifications

  Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

  Read Less

  Apply Now

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