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. FairMoney maintains a strong international presence, with offices in several countries, including France, Nigeria, Germany, Latvia, the UK, Türkiye, and India.

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.

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

• Paid Time Off (25 days Vacation, Sick & Public Holidays) to all B2B contractors and employment staff.
• Family Leave (Maternity, Paternity)

• Training & Development budget
• Paid company business trips (not mandatory)
  
• Contract: permanent or B2B
• Location: any, within 3 hours difference from CET.
• Remote work: any combination of remote / office work is acceptable.

Recruitment Process

• Screening call with Talent Manager
• Home Test assignment
• Technical interview with Head of Data Science (once test assignment stage is passed)
• Interview with the team and key stakeholders.