A financial institution’s ability to make good lending decisions depends on more than how much money it has available to lend. It also depends on how accurately it can assess customers, understand their financial behaviour, estimate repayment capacity and identify potential credit risks.
For digital financial services companies, these decisions increasingly rely on data science, statistical modelling and machine learning. The right models can help financial institutions extend credit to suitable customers while managing defaults and protecting the long-term health of their lending portfolios.
Moniepoint Incorporated is recruiting a Senior Data Scientist (Credit) to strengthen its consumer lending and credit decision-making capabilities. The successful candidate will develop and deploy analytical models that influence how customers are assessed, how credit offers are structured and how lending performance is managed.
This is a senior-level opportunity for someone with a strong background in data science, financial services and credit risk analytics. It is particularly relevant to professionals who have worked with consumer lending models and understand how statistical methods can support real financial decisions.
The position is advertised as remote in Nigeria.
Position Summary
| Job Information | Details |
|---|---|
| Position | Senior Data Scientist (Credit) |
| Organisation | Moniepoint Incorporated |
| Location | Nigeria (Remote, as advertised) |
| Job Type | Full-time |
| Experience | 5+ years |
| Qualification | Degree or relevant qualification in a quantitative discipline |
| Required Technical Skills | SQL, Python or R, machine learning and statistical modelling |
| Industry | Banking, Financial Services and Fintech |
| Salary | Not publicly specified |
| Application Deadline | Not specified |
Why This Role Matters
Consumer lending involves a series of decisions. A financial services company must determine who qualifies for credit, how much each customer can borrow, what terms are appropriate and how to respond when repayment behaviour changes.
Making these decisions consistently across a large customer base requires reliable data and well-designed risk models.
This is where the Senior Data Scientist (Credit) comes in.
At Moniepoint, the role sits within the Consumer Credit team and focuses on developing models and experiments that support the company’s lending activities. Rather than simply analysing historical information, the successful candidate will help translate data into systems and decisions that can be used across the business.
The work will influence credit scoring, affordability assessment, pricing, collections and customer retention.
It also involves an important balance: making credit accessible to more customers without exposing the business to unnecessary lending risks.
What You Will Be Working On
The position combines several areas of data science and financial risk management. Understanding these responsibilities will help applicants assess whether their experience matches what the employer needs.
1. Developing Credit Scoring and Risk Assessment Models
One of the central responsibilities is building and deploying credit scoring models.
A credit scoring model uses available customer information and relevant historical data to estimate the likelihood that a borrower will repay a loan.
The successful candidate will develop models that help the business assess customers and support lending decisions.
This requires a strong understanding of statistical techniques, predictive modelling and the factors that influence repayment behaviour.
The models must also be suitable for practical deployment rather than existing only as research projects or experimental notebooks.
2. Modelling Customer Affordability and Behaviour
Creditworthiness and affordability are related, but they are not exactly the same.
A customer may have a history of borrowing and repayment, but the amount they can reasonably afford to repay depends on their broader financial circumstances.
The role therefore involves developing affordability and behavioural models that help the company understand customers more accurately.
These models can support decisions about appropriate credit limits and personalised lending offers.
Customer behaviour is also important after a loan has been issued. Changes in repayment patterns, account activity and other relevant indicators can provide useful information for managing credit risk.
3. Improving Credit Pricing and Lending Decisions
The amount offered to a customer and the terms attached to a credit product can influence both customer experience and the financial performance of the lending business.
The Senior Data Scientist will contribute to credit pricing and credit limit modelling.
This means using data to help determine how lending offers should be structured for different customer profiles.
The objective is to support responsible lending decisions that balance customer access to credit with sustainable portfolio performance.
4. Designing Experiments and Measuring Results
The position requires the ability to design and conduct experiments that improve lending performance.
For example, a financial services company may want to understand whether changing a credit approval threshold improves the quality of its lending portfolio without unnecessarily excluding suitable customers.
The data scientist can design an experiment, compare outcomes and use the findings to recommend a better approach.
The employer specifically highlights the need to optimise:
- Credit approval rates.
- Loan loss rates.
- Lending profitability.
- Overall credit portfolio performance.
Experience with A/B testing and experimentation is therefore an important part of the role.
5. Integrating Models Into Real-Time Systems
A predictive model is most useful when its output can support actual business decisions.
The successful candidate will work with product teams to integrate credit decision logic into real-time systems.
This involves collaborating with professionals responsible for product development and technical implementation to ensure that models can be used within the company’s lending processes.
Applicants should understand the difference between developing a model and preparing it for practical deployment.
Experience working with cross-functional product or engineering teams will be valuable.
6. Supporting Collections and Customer Retention
The responsibilities extend beyond deciding whether a customer should receive credit.
The company also needs to understand what happens after a lending decision has been made.
The Senior Data Scientist will develop models that help improve collections outcomes, manage customer churn and support retention.
For collections, data can help identify patterns associated with repayment difficulties and support more informed approaches to managing outstanding obligations.
For retention, behavioural analysis can help the business understand why customers disengage and identify opportunities to improve the customer experience.
These activities contribute to a healthier lending portfolio and stronger customer relationships.
7. Maintaining Data Quality and Responsible Model Use
Credit decisions can have significant consequences for both financial institutions and their customers.
Models must therefore be developed and used responsibly.
The successful candidate will help ensure that data quality, regulatory considerations and ethical use of models are properly considered across decision-making processes.
This includes paying attention to the reliability of the information used to train and evaluate models and understanding the implications of the decisions they support.
Knowledge of consumer lending regulations and credit risk management will be particularly relevant.
8. Working With and Mentoring Product Teams
The position is not limited to individual technical assignments.
The Senior Data Scientist will also work with product squads and help strengthen their understanding of experimentation and data-driven decision-making.
This requires the ability to explain analytical findings in a way that colleagues outside the data science function can understand and use.
The successful candidate should be comfortable discussing technical approaches, explaining model outcomes and making practical recommendations to business stakeholders.
Who Moniepoint Is Looking For
This is not an entry-level data analytics position. The company is seeking a professional with substantial experience applying data science within financial services.
Applicants should have:
A quantitative academic background
A degree or relevant qualification in Statistics, Mathematics, Engineering or another quantitative discipline is required.
The role involves statistical analysis and mathematical modelling, so applicants should be comfortable working with quantitative methods.
At least five years of relevant experience
Candidates should have more than five years of experience in data science, decision science or risk analytics within financial services.
Experience in consumer lending and credit risk will be particularly relevant.
Knowledge of credit risk and consumer lending
Applicants should understand how lending decisions are made, how credit risk is assessed and how financial institutions manage the risks associated with consumer credit.
Knowledge of relevant regulatory considerations is also expected.
Strong SQL skills
SQL is important for retrieving, querying and analysing structured data.
The successful candidate should be comfortable working with datasets and extracting information needed for modelling and business analysis.
Programming and modelling experience
Proficiency in SQL and at least one modelling or programming language is required.
The employer specifically mentions:
- Python.
- R.
Applicants should be able to demonstrate practical experience using at least one of these languages for statistical analysis, predictive modelling or machine learning.
Experience with machine learning and experimentation
The role requires familiarity with machine learning, A/B testing, collections modelling and churn management.
Candidates should be able to explain the methods they have used and how their work contributed to better business decisions.
Communication and collaboration
Technical knowledge alone will not be sufficient.
The successful candidate must be able to translate complex analytical findings into clear recommendations and work effectively with colleagues across different teams.
Technical Skills That Could Strengthen Your Application
Professionals considering this opportunity should pay particular attention to the relationship between their technical experience and the company’s credit modelling requirements.
The following areas are especially relevant:
| Technical Area | Why It Matters |
|---|---|
| Statistical modelling | Supports the development and evaluation of predictive models. |
| Python or R | Used for data analysis, modelling and machine learning. |
| SQL | Helps retrieve and analyse financial and customer datasets. |
| Credit scoring | Supports customer risk assessment and lending decisions. |
| Machine learning | Helps identify patterns and improve predictive performance. |
| A/B testing | Allows lending strategies and decision approaches to be evaluated. |
| Collections modelling | Supports improved management of repayment outcomes. |
| Churn modelling | Helps identify customers at risk of disengagement. |
| Model deployment | Makes analytical models useful in operational systems. |
Applicants do not need to treat this table as a separate list of qualifications. Rather, it highlights the areas they should be prepared to discuss during the recruitment process.
What Makes This a Senior-Level Opportunity?
The title of the position reflects more than years of experience.
Moniepoint is looking for someone who can take ownership of important modelling work and connect technical decisions with business outcomes.
A candidate may be very comfortable writing Python code or building statistical models, but this role also requires an understanding of how those models affect lending decisions, customer experience and portfolio performance.
For example, improving a credit approval model is not simply a matter of increasing the number of approved applications. The company must also consider repayment performance and potential losses.
Similarly, a model that identifies customers likely to disengage is useful only when the business can translate that information into appropriate retention strategies.
The successful candidate will therefore need to combine technical depth with sound business judgement.
Benefits and Professional Development
Moniepoint states that successful employees can expect a competitive compensation package that includes salary, pension, health insurance and monthly bonuses.
The organisation also highlights its focus on employee learning and development.
Its stated approach includes knowledge sharing, training and regular internal technical discussions.
For an experienced data scientist, this provides an opportunity to work in a financial technology environment where data science contributes directly to lending and product decisions.
The exact salary for this vacancy has not been publicly disclosed.
Recruitment and Selection Process
Candidates should prepare for a technical recruitment process that assesses both theoretical knowledge and practical programming ability.
The employer outlines the following stages:
1. Preliminary recruiter conversation
The process begins with an initial phone call with a recruiter. This is an opportunity for the company to understand the candidate’s background and assess general suitability.
2. HackerRank coding assessment
Applicants will complete a coding exercise covering core data science concepts, including mathematics, statistics, linear algebra and Python fundamentals.
The programming assessment also covers data structures and algorithms.
3. Take-home assignment
Candidates will be given an assignment to complete outside the interview setting.
This stage allows the recruitment team to assess how applicants approach a practical analytical problem.
4. Technical interview
A Lead in the Data Science team will review the take-home assignment in detail.
Candidates should be prepared to explain their methodology, assumptions, modelling choices and conclusions.
5. Behavioural and technical interview
The final stage involves a discussion with the hiring manager, covering technical competence and how the candidate approaches work, collaboration and problem-solving.
Applicants should prepare to discuss previous projects in detail rather than relying only on general descriptions of their experience.
How to Prepare a Strong Application
A senior data science application should make it easy for the recruitment team to understand the candidate’s technical capabilities and professional impact.
Your CV should clearly identify the financial services projects you have worked on, the types of models you have developed and the tools you used.
Where possible, explain the business problem behind each project and the outcome of your work.
For example, instead of writing that you developed predictive models, describe the type of model, the decision it supported and how its performance was evaluated.
You should also be ready to discuss:
- Your experience developing and deploying credit risk models.
- How you have used Python or R for modelling.
- Your approach to evaluating model performance.
- Previous work involving consumer lending or affordability assessment.
- Your experience with A/B testing and experimentation.
- How you have worked with product and engineering teams.
- Examples of translating complex analysis into business recommendations.
Avoid including confidential customer information or proprietary datasets in your portfolio. You can explain your contribution using anonymised examples and appropriate performance indicators.
How to Apply for the Senior Data Scientist (Credit) Position
Interested candidates can access the recruitment form through Moniepoint’s official careers platform.
Official Application Portal: USE THIS LINK TO APPLY
The application form requests basic personal information, contact details, a CV and a LinkedIn profile. A cover letter and personal website are optional fields.
Candidates should also be prepared to answer questions about their previous employment with Moniepoint and their practical experience in consumer lending and credit risk modelling.
Important eligibility clarification: Although the vacancy is advertised as a remote position in Nigeria, the current official application form asks whether applicants have the legal right to work in India and whether they are currently based in India. Nigerian applicants should verify the intended geographical eligibility with Moniepoint before proceeding.
Application Deadline: Not specified in the published vacancy.
Final Word
The Senior Data Scientist (Credit) position at Moniepoint is an opportunity for an experienced data professional to work on problems that sit at the centre of financial technology: lending decisions, credit risk, customer behaviour and portfolio performance.
The successful candidate will need more than general data analysis experience. Strong statistical knowledge, practical programming skills and a sound understanding of consumer lending are central to the position.
For professionals with five or more years of relevant financial services experience, this is a role worth examining closely. However, applicants should confirm the geographical eligibility issue on the official application form before investing time in the recruitment process.
Israel Otoijamun is a digital business strategist, technology entrepreneur, and the founder of Xcrow, a platform connecting businesses with top remote talent.
With more than 15 years of experience across online platforms, technology, and digital payments, Israel regularly publishes insights on freelancing, tech job opportunities, remote workforce strategies, and modern hiring ecosystems.
Read more about Israel Otoijamun.
