Latest Jobs at Credit Direct Limited

Job Summary

• As a Data Scientist, you will play a key role in driving data-driven decisions and supporting business objectives. You will be responsible for developing data models, conducting advanced analyses, and extracting actionable insights to support business objectives. Your expertise in Python, machine learning libraries, web frameworks like Flask and FastAPI will be essential, along with your experience working with financial data, particularly in credit lending and credit risk.

Job Details

RESPONSIBILITIES:

Data Analysis & Modelling:

• Develop predictive models to assess credit risk and lending outcomes.
• Utilize Python libraries (e.g., Pandas, Scikit-Learn, TensorFlow) to build, train, and evaluate machine learning models.

Machine learning Model Deployment:

• Design, develop, and deploy credit-risk decisioning models as APIs using frameworks such as Flask or FastAPI.
• Ensure that deployed models are scalable, maintainable, and easily accessible for other teams via RESTful APIs.
• Monitor and maintain model performance post-deployment, ensuring high accuracy, availability and reliability.

Data Collection & Pre-processing:

• Clean large financial datasets, including credit lending and credit risk data, ensuring data is structured, and ready for analysis.

Data Governance & Compliance:

• Ensure compliance with data privacy and security standards, when working with sensitive financial and credit data.
• Document data sources, methodologies, and model parameters to ensure transparency and reproducibility.

Large Language Models:

• Apply LLMs and other advanced NLP techniques to extract insights from unstructured financial text data.
• Integrate LLM Chatbots with CRM systems and other in-house tools to improve customer satisfaction.

Requirements

• Bachelor’s degree in Computer Science, Mathematics, Statistics, or a related field. 
• Relevant Certifications (e.g., IBM Data Science Professional Certificate, Microsoft Certified: Azure Data Scientist Associate, Tensorflow Developer Certificate) are an added advantage.
• 1 - 3 years of experience in data science or a related field, preferably within the financial services sector.
• Experience working with credit lending, credit risk or other financial datasets is highly preferred.
• Proven experience with Python for data science applications, including libraries such as Pandas, Scikit-Learn, and TensorFlow.
• Familiarity with large language models (LLMs) and NLP techniques is a plus.

COMPETENCIES REQUIREMENTS:

Technical:

• Statistical Analysis & Modelling: Strong knowledge of statistical and machine learning techniques to create models that support risk assessment and lending decisions.
• Programming & Scripting: Proficiency in Python for data manipulation, model building, and automation.
• Cloud Computing: Experience with GCP and AWS for data storage, model deployment, and scalable computing.
• Financial Data Analysis: Understanding of credit lending and credit risk data, with the ability to work within the regulatory constraints of financial data.
• LLM & NLP (Nice to Have): Familiarity with large language models for analysing unstructured text data in financial contexts.

Tools:

• Python , Jupyter Notebooks, TensorFlow, PyTorch, Scikit-Learn, Apache Spark, SQL, FastApi, Flask

Behavioural:

• Analytical Skills: Ability to translate business needs into data-driven solutions and interpret model results accurately.
• Collaboration: Strong team collaboration skills for working with analysts, engineers, and business stakeholders.
• Attention to Detail: Accuracy in data handling, model development, and documentation for financial data analysis.
• Communication: Ability to present complex analytical findings in a clear, concise manner for diverse audiences.

What to Expect in the Hiring Process:

• A preliminary phone call with the recruiter
• Technical interview 
• Assessment
• Interview with Senior members of the team
• Cultural and Behavioural Fit Interview with a member of the Executive team.

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Job Summary

• We are looking to fill the role of an Analytics Engineer. The role holder will be responsible for developing, maintaining, and optimizing data pipelines, ensuring data quality, and supporting analytics initiatives. This role requires expertise in data modelling, data transformation, and data governance to generate insights that support business decision-making.

Job Details

RESPONSIBILITIES:

Data Modelling & Schema Design:

• Develop and implement data models that support analytics and reporting requirements, ensuring scalability, performance, and data accuracy.
• Work with stakeholders to translate business requirements into logical and physical data models, creating well-structured schemas that align with company standards.

Data Infrastructure Management:

• Optimize database structures (e.g., indexing, partitioning) to enhance query performance and improve data accessibility.
• Ensure data reliability, security, and scalability to support business needs and data-driven applications.

Analytics & Reporting Support:

• Develop and maintain dashboards, reports, and other visualizations to deliver insights that drive key business decisions.
• Collaborate with data analysts to translate business questions into metrics and key performance indicators (KPIs).
• Continuously improve reporting capabilities to support evolving business objectives.

Data Quality & Governance:

• Implement data governance and security measures to protect sensitive information.
• Maintain documentation for data sources, transformations, and definitions to facilitate consistent data usage.

Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related field. Equivalent experience may be considered.
• 1 – 3+ years in data engineering, analytics engineering, data analyst, or a similar field.
• Proven experience with SQL databases (MySQL, MSSQL, BigQuery, and Amazon RDS) and NoSQL databases (e.g, MongoDB) 
• Experience with data transformation tools such as dbt, AWS glue, Dataform, and scripting languages like Python.
• Proven experience with workflow orchestration tools like Apache Airflow, Prefect, Dagster, and data integration tools like Airbyte.
• Prior experience with cloud platform, particularly GCP and/or AWS

COMPETENCIES REQUIREMENTS:

Technical:

• Database Management & Modeling: Proficiency in data pipeline development, data warehousing, data modelling, and transformation using dbt and other related transformation tools.
• Cloud Infrastructure Management: Strong understanding of cloud data management in GCP and AWS.
• Strong knowledge of and experience with Visualization and business intelligence tools like PowerBI, Tableau, or  Looker studio.
• Database Management: Experience in SQL (BigQuery, Amazon RDS) and NoSQL databases (MongoDB, DynamoDB).
• Programming & Scripting: Proficiency in SQL and Python for data processing and automation.
• Experience working with financial data is a plus.

Tools: 

• SQL, dbt (Data Build Tool), Python, Airbyte, Apache Airflow, BigQuery, Amazon RDS, MongoDB, PowerBI/Tableau, Terraform

Behavioural:

• Analytical Skills: Ability to solve complex data problems and improve data pipeline efficiency.
• Collaboration: Skilled in working cross-functionally with data analysts, engineers, and business stakeholders.
• Attention to Detail: Accuracy in data transformations and adherence to data governance standards.
• Communication: Strong verbal and written communication skills to document processes and share insights.

What to Expect in the Hiring Process:

• A preliminary phone call with the recruiter
• Technical interview 
• Assessment
• Interview with Senior members of the team
• Cultural and Behavioural Fit Interview with a member of the Executive team.

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Job Summary

To design, implement, and maintain efficient and scalable infrastructure and CI/CD pipelines, utilizing tools such as AWS, Docker, Kubernetes, GitHub Actions, and Terraform. The DevOps Engineer will also focus on enhancing system reliability and automating processes through Infrastructure as Code (IaC) to support continuous integration and deployment across all development environments.

Job Details

RESPONSIBILITIES:

• Design, develop, and manage scalable, high-performance cloud infrastructure preferably AWS
• Implement containerization and orchestration solutions using Docker and Kubernetes.
• Manage infrastructure as code (IaC) using Terraform to ensure reproducibility and consistency across all environments
• Automate monitoring, logging, and alerting to maintain high system availability.
• Collaborate with engineering teams to ensure seamless development, testing, integration and deployment of applications.

Requirements

• Minimum of 4 years of experience in DevOps or a similar role, with proven expertise in AWS, Docker, Kubernetes, GitHub Actions, Terraform, and other DevOps-related services.

COMPETENCIES REQUIREMENTS:

Technical:

• Proficiency in cloud security tools and technologies (e.g., AWS GuardDuty, Azure Security Center).
• Strong understanding of cybersecurity frameworks (e.g., NIST, ISO 27001) and data privacy regulations (e.g., GDPR).
• Experience with SIEM solutions, firewalls, IDS/IPS, and endpoint protection tools.
• Proficient in scripting languages like Python or Bash for automating security tasks.

Behavioural:

• Strong analytical and problem-solving skills.
• Excellent communication skills to interact effectively with both technical and non-technical stakeholders.
• Detail-oriented, with a proactive approach to identifying and mitigating potential security risks.

What to Expect in the Hiring Process:

• A preliminary phone call with the recruiter
• Technical interview 
• Assessment
• Interview with Senior members of the team
• Cultural and Behavioural Fit Interview with a member of the Executive team.

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Job Summary

• The primary responsibility of this role is to oversee and ensure effective loan repayment management, including reconciliation, analysis, and reporting on loan portfolio performance. The role involves proactive engagement with customers, employers, and other key stakeholders to support prompt repayments and loan recovery. This position is critical in identifying portfolio trends, providing insights, and ensuring the integrity and performance of the loan portfolio.

Job Details

Key Responsibilities:

Loan Repayment Management:

• Ensure that all loan repayments are received promptly, accurately, and in line with repayment schedules.
• Regularly reconcile repayments to identify and address discrepancies.

Portfolio Reconciliation and Analysis:

• Conduct in-depth reconciliation and analysis of the loan portfolio, including performance metrics for all active loans.
• Prepare and present detailed reports on portfolio performance to provide a clear view of repayment trends and issues.

Stakeholder Engagement and Follow-Up:

• Actively monitor and follow up with employers, customers, and other stakeholders to secure timely repayments.
• Maintain open communication channels to address and resolve repayment issues quickly.

Loan Recovery and Write-Off Management:

• Monitor and ensure recovery efforts on written-off loans, collaborating with the collections team to achieve recovery targets.
• Develop and implement strategies to minimize loan loss and maximize recovery on delinquent accounts.

Feedback and Reporting on Portfolio Health:

• Provide regular feedback to relevant teams on portfolio-related issues and emerging trends.
• Generate insights and recommendations to improve repayment performance and support informed decision-making.

Requirements

Qualifications and Skills:

• Bachelor’s degree in Finance, Accounting, Business Administration, or a related field.
• At least 3 years of experience in loan management, finance, or a similar role.
• Strong analytical and reconciliation skills with a keen eye for detail.
• Excellent communication and stakeholder management abilities.
• Proficiency in financial software and Microsoft Excel.
• Experience with loan recovery and collections is an advantage.

What to Expect in the Hiring Process:

• A preliminary phone call with the recruiter
• Technical interview 
• Assessment
• Interview with Senior members of the team
• Cultural and Behavioral Fit Interview with a member of the Executive team.