Credit Direct Limited is a non-bank finance company with its Head-Quarters in Lagos, Nigeria. The company was established in 2006 and is focused on providing Payroll based consumer loans to eligible individuals. The Company currently operates in 25 states in Nigeria including the Federal Capital Territory– Abuja. With a staff strength of over 1000 employees and an active customer base in excess of 300,000, Credit Direct Limited is positioning itself to become the dominant market leader in the unsecured micro-lending (payroll lending) space in Nigeria and indeed Sub-Saharan Africa.
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.
Method of Application
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