Data Science Manager - Credit

About Moniepoint

Moniepoint is an integrated digital financial services platform catering to businesses in emerging markets. Since its inception in 2019, Moniepoint's technology has supported over one million businesses by delivering comprehensive tools encompassing payments, banking, credit, and business management essential for their success.

Through its Nigerian subsidiaries, Moniepoint MFB and TeamApt Ltd, the company handles the majority of POS transactions in Nigeria, managing in excess of $170 billion in annualized total payment volume. Recognized as Africa's second-fastest growing fintech, Moniepoint is profitable and was honored by the Central Bank of Nigeria in 2022 as the most inclusive payment platform nationwide.

Role and Responsibilities

• Build, validate, and deploy advanced attribution and predictive algorithms utilizing cutting-edge machine learning techniques and mathematical optimization models.
• Focus on key areas including attribution modeling, Customer Acquisition Cost (CAC) and marketing CAC estimation, Lifetime Value (LTV) forecasting, marketing mix modeling, and multichannel attribution and experimentation.
• Plan and analyze experiments to extract detailed, actionable insights to guide business decisions.
• Collaborate effectively with diverse teams across product, engineering, and business units to align strategies and execute projects.
• Take ownership of production marketing attribution models to improve budgeting efficiency and marketing effectiveness.

Candidate Profile and Requirements

• Minimum of five years professional experience in quantitative analysis roles, excluding academic or internship positions; experience in FMCG or fintech sectors is highly desirable.
• Possession of at least a Master of Science degree, with preference given to candidates holding a PhD in scientific disciplines such as Physics, Statistics, Engineering, Computer Science, or Mathematics.
• Strong theoretical and practical expertise in statistical inference, Bayesian statistics, causal inference, time series analysis, and mathematical optimization.
• Significant hands-on experience with Multichannel Attribution Modeling and Marketing Mix Modeling.
• Proficiency in Python programming along with popular libraries and tools including Pandas, SciPy, XGBoost, Jupyter/iPython notebooks, and modeling frameworks like PyMC3.
• Comprehensive knowledge of the entire predictive and prescriptive modeling lifecycle, from defining problems to deploying and maintaining models, with strong data modeling and SQL skills, plus familiarity with cloud computing infrastructures.
• Proven capability in designing, conducting, and interpreting digital product experiments such as A/B tests and multivariate tests.
• Ability to apply advanced statistical modeling and analytics techniques to generate insights that influence product development.
• Excellent command of spoken and written English.