Founding Machine Learning Engineer, Accentity
Nigeria· Remote · 2 months agoAbout us
At Accentity, we believe everyone should have equitable access to financial services. So, we’re reimagining credit reporting for underserved populations globally. We’re starting in one of the most difficult markets but were it is also most needed. Only14% of the Nigerians have a credit history, we’re here to do the same for the other 84%.
About the role
We’re looking for a skilled Machine Learning Engineer to join us (Co-Founders) and lead the development of credit and risk modelling solutions for underserved populations. In this role, you will work with diverse, non-traditional data sources to build fair and inclusive credit assessment models that expand financial access while maintaining robust risk management practices.
About the day-to-data
At Accentity, we believe everyone should have equitable access to financial services. So, we’re reimagining credit reporting for underserved populations globally. We’re starting in one of the most difficult markets but were it is also most needed. Only14% of the Nigerians have a credit history, we’re here to do the same for the other 84%.
About the role
We’re looking for a skilled Machine Learning Engineer to join us (Co-Founders) and lead the development of credit and risk modelling solutions for underserved populations. In this role, you will work with diverse, non-traditional data sources to build fair and inclusive credit assessment models that expand financial access while maintaining robust risk management practices.
About the day-to-data
- Model Development & Implementation
- Design and develop machine learning models for credit scoring and risk assessment using both traditional and alternative data sources
- Build predictive models that incorporate data from credit unions, cooperatives, utility providers, and consumer self-reported financial activity
- Implement feature engineering pipelines to extract meaningful signals from unstructured and semi structured data
- Optimise model performance while ensuring fairness and regulatory compliance across diverse demographic groups
Data Engineering & Integration
- Develop robust ETL pipelines to integrate data from multiple disparate sources including financial institutions, utility companies, and consumer platforms
- Clean, preprocess, and standardise large volumes of unorganised data from various formats and systems
- Implement data quality monitoring and validation frameworks to ensure model reliability
- Design scalable data architecture to support real-time and batch processing requirements
Model Validation & Governance
- Conduct comprehensive model validation including backtesting, stress testing, and bias analysis
- Develop model monitoring systems to track performance degradation and data drift
- Create detailed model documentation and reports for regulatory compliance and internal stakeholders
Implement A/B testing frameworks to measure model impact on business outcomes
Must Have Skills
Must Have Skills
- Proficient in Python and mySQL for data manipulation and model development
- 3 years+ experience with machine learning frameworks (scikit-learn, XGBoost, LightGBM) and deep learning libraries (TensorFlow/PyTorch)
- Solid understanding of statistical methods, probability theory, and econometrics
- Experience with feature engineering, model validation, and performance evaluation metrics specific to credit risk (AUC, KS statistic, Gini coefficient)
- Knowledge of model interpretability techniques and bias detection/mitigation strategies
- Proficiency in data cleaning, preprocessing, and handling missing/inconsistent data from multiple sources
- Experience with imbalanced data handling (e.g., SMOTE, class weighting).
- Experience with ETL pipelines and data integration from disparate systems
- Knowledge of data quality assessment and validation techniques
- Experience with cloud platforms (AWS, GCP, Azure) and MLOps practices
- Experience working in Nigeria and/or elsewhere in Sub-Sahara Africa
Desirable Skills
- Experience with ensemble methods, time series analysis, and survival analysis for credit modelling
- Knowledge of causal inference methods and A/B testing for model impact assessment
- Familiarity with graph neural networks or network analysis for relationship-based features
- Experience with fairness aware machine learning and algorithmic bias testing
- Knowledge of explainable AI techniques and tools (e.g., SHAP, LIME) for regulatory compliance
- Strong communication skills to explain complex models to non technical team member or partners
- Experience collaborating with cross functional teams including product, compliance, and business development
- Ability to work with messy, real-world data and adapt to changing requirements
Please note - this is currently an unpaid role where we work around your availability. This is also potentially a Co-Founder role.
To apply, please email hello@accentity.tech and explain why you want to work with us (500 words maximum).