Data Scientist, Risk
Flutterwave · Risk team
- Data Scientist
- Lekki, Lagos
- Full-time
- 3–5 years
At a glance
About the company
Flutterwave is a pan-African payments company. Its payment infrastructure lets businesses and consumers in Africa and abroad make and receive payments across borders, and its Nigerian office is in Lekki, Lagos.
Description
You join Flutterwave's Risk team as it moves from studying fraud after it happens to predicting it before it does. You will:
- Design, train and tune machine learning models for fraud detection, transaction monitoring and risk
- Clean and explore very large transaction data sets to find features that predict fraud
- Work with Engineering and MLOps to deploy models, watch for data drift and retrain them
- Turn model scores into clear rules with Compliance and Operations, balancing safety against customer friction
- Check that data in the risk and machine learning pipelines is accurate and consistent
- Document models, training data and decisions for audits and regulators
Qualifications & Skills
Must have 6
- A bachelor's or master's degree in computer science, statistics, mathematics, data science or a related field
- 3–5 years as a data scientist building and deploying machine learning models in production
- Experience in fintech or payments
- Strong Python and SQL
- Deep use of ML libraries such as scikit-learn, XGBoost, LightGBM, TensorFlow or PyTorch
- Experience with a cloud data warehouse such as Redshift, Snowflake or BigQuery
Nice to have 1
- Direct work on fraud detection, credit risk or anti-money laundering (AML) models
How to stand out for this job
Written by our team from the employer's post. Work through it in order, then apply.
1What they actually want
Our plain-words read of this job. It explains the job, not the employer's own words.
Flutterwave moves money for businesses across Africa and beyond, and every day some of those payments are fraudulent. This job sits in the Risk team and is about catching them early. You will dig through very large transaction data sets, build features and train models that flag suspicious payments, then work with engineers to put those models into production. The work does not stop at launch: you watch how each model performs, spot when the data shifts, retrain it, and write it up so auditors and regulators can follow it.
They want three to five years of real production machine learning, not only notebooks, and experience in fintech or payments is a must. Strong Python and SQL, libraries such as XGBoost or LightGBM, and a cloud data warehouse are the core tools. To stand out, show a model you deployed and what it did to fraud losses or false alarms, and show that you can explain a model's trade-offs to legal, finance and operations people. Past fraud, AML or credit risk models count strongly in your favour.
2Words to use in your CV
Use the words that are true for you. The dark ones matter most for this job.
- fraud detection
- machine learning
- transaction monitoring
- feature engineering
- model deployment
- MLOps
- data drift
- Python
- SQL
- XGBoost
- LightGBM
- scikit-learn
- Redshift
- risk strategy
- AML
- payments
4Tailor your portfolio
Make a copy of your portfolio for this job and lead with these three projects. Give the problem, the data, the tools and the result as a number.
- 01
A fraud detection model
A model on a public card fraud data set, showing how you handled the rare fraud cases and chose the alert threshold.
- 02
A drift monitoring notebook
A check that compares this month's transaction data with the training data and flags when the model needs retraining.
- 03
From model score to rules
A short write-up turning model scores into simple block, review and allow rules, with the trade-off for customers.
3Tailor your CV
- Headline“Data Scientist | Fraud Detection, Machine Learning, Payments”
- Lead with productionPut models that went live first, and say how long they ran and how you monitored them.
- Show the business effectGive fraud caught, losses avoided or false alarms cut, as numbers.
- Name your stackList Python, SQL, XGBoost or LightGBM and the warehouse you used, in that order.
- Show you can explainMention a time you turned a model score into rules that operations or compliance used.
BeforeWorked on fraud detection models.
AfterDeployed an XGBoost fraud model on 2 million monthly card payments, catching 35% more fraud with 20% fewer false alarms.
Example only. Use your own real numbers.
5Learn the missing skill
SQL and Python for Data Analysis
Live cohort · Starts 4 Nov 2026
₦200,000 or 4 × ₦50,000
6Before you apply
Apply on Flutterwave's careers page
You'll leave Exalda. Your application goes straight to the employer, and we never charge you to apply.
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