FairMoney Microfinance Bank is the number 1 most downloaded fintech app in Nigeria. With over 10,000 daily loan disbursements, and over 5 million users enjoying banking, savings, and investment services, FairMoney helps the average Nigerian access finance tools to take control of both their life and their finances.
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Your mission is to develop data science-driven algorithms and applications to improve decisions in business processes like risk and debt collection, offering the best-tailored credit services to as many clients as possible.
Requirements
Strong background in Mathematics / Statistics / Econometrics / Computer science or related field
5+ years of work experience in analytics, data mining, and predictive data modelling, preferably in the fintech domain
Being best friends with Python and SQL
Hands-on experience in handling large volumes of tabular data
Strong analytical skills: ability to make sense out of a variety of data and its relation/applicability to a specific business problem
Feeling confident working with key Machine learning algorithms (GBM, XG-Boost, Random Forest, Logistic regression)
Being at home building and deploying models around credit risk, debt collection, fraud, and growth.
Track record of designing, executing and interpreting A/B tests in business environment.
Strong focus on business impact and experience driving it end-to-end using data science applications.
Strong communication skills
Being passionate about all things data.
Our tool stack
Programming language: Python
Production: Python API deployed on Amazon EKS (Docker, Kubernetes, Flask)
ML: Scikit-Learn, LightGBM, XGBoost, shap
ETL: Python, Apache Airflow
Cloud: AWS, GCP
Database: MySQL
DWH: BigQuery, Snowflake
BI: Tableau, Metabase, dbt
Streaming Applications: Flink, Kinesis
Role and Responsibilities
Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions
Mine and analyze data from company databases and external data sources to drive optimization and improvement of risk strategies, product development, marketing techniques, and other business decisions
Assess the effectiveness and accuracy of new data sources and data gathering techniques
Use predictive modelling to increase and optimize customer experiences, revenue generation, and other business outcomes
Coordinate with different functional teams to make the best use of developed data science applications
Develop processes and tools to monitor and analyze model performance and data quality
Apply advanced statistical and data mining techniques in order to derive patterns from the data
Own data science projects end-to-end and proactively drive improvements in both data
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