FairMoney is a pioneering mobile banking institution specializing in extending credit to emerging markets. Established in 2017, the company currently operates primarily within Nigeria, and it has secured nearly €50 million in funding from renowned global investors, including Tiger Global, DST, and Flourish Ventures.

In alignment with its vision, FairMoney is actively constructing the foremost mobile banking platform and point-of-sale (POS) solution tailored for emerging markets. The journey began with the introduction of a digital microcredit application exclusively available on Android and iOS devices. Today, FairMoney has significantly expanded its range of services, encompassing a comprehensive suite of financial products, such as current accounts, savings accounts, debit cards, and state-of-the-art POS solutions designed to meet the needs of both merchants and agents.

FairMoney thrives on its diverse workforce, bringing together talent from over 27 nationalities. This multicultural team drives the company's mission of reshaping financial services for underserved communities.To gain deeper insights into FairMoney's pivotal role in reshaping Africa's financial landscape, we invite you to watch informative video.

Job Summary:

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

Benefits