Data Scientist at Payswitch

The role is responsible for developing, validating, and maintaining data-driven models and analytical frameworks that support PaySwitch's credit platforms, internal decision-making, product analytics, and operational intelligence. This is a hands-on individual contributor role requiring strong analytical thinking, practical modelling skills, and close collaboration with data engineering, product, and business teams.

Position in Organisation

• Reports To: Head of Product, Innovation & Excellence

Duties & Responsibilities

Modelling & Advanced Analytics

• Design and develop analytical and predictive models for credit scoring, risk assessment, internal performance analytics, and customer/transaction behavior analysis.
• Build and evaluate regression and classification models including logistic and linear regression, decision trees, random forests, and gradient boosting models where appropriate.
• Select modelling techniques based on interpretability, robustness, and business impact.

Feature Engineering & Data Exploration

• Perform exploratory data analysis (EDA) across internal and external datasets.
• Design features from transactional, behavioral, operational, and demographic data.
• Collaborate with the Data Engineer to ensure features are reproducible and production ready.

Validation, Explainability & Governance

• Conduct model validation, performance testing, and stability analysis.
• Monitor model and analytical outputs for drift, bias, and degradation.
• Produce clear documentation covering assumptions, feature rationale, metrics, and limitations.
• Support internal reviews, audits, and decision forums.

Product & Platform Collaboration

• Work closely with product teams to translate analytical insights into product requirements and UX decisions.
• Contribute to Product and Functional Requirement Documents by defining analytical logic, thresholds, KPIs, and measurement frameworks.
• Support experimentation, A/B testing, and hypothesis-driven product improvements.
• Support deployment of models and analytics into production environments with engineering teams.
• Continuously improve analytical methods, tools, and practices.

Skills & Abilities

Technical

• Strong proficiency in Python for data analysis and modelling.
• Working experience with SQL (advanced querying).
• Experience with modern machine learning libraries and frameworks.
• Familiarity with Azure data and analytics services: Azure Data Lake, Azure Synapse Analytics, Azure Machine Learning (working level), Microsoft Fabric (working knowledge).
• Comfortable working with version control (Git) and collaborative workflows.

Non-Technical

• Strong analytical and critical thinking skills.
• Ability to explain complex concepts clearly to non-technical stakeholders.
• Structured documentation and communication habits.
• High ownership, curiosity, and accountability.
• Ability to work across multiple problem domains simultaneously.

Education & Experience

• Degree in Data Science, Statistics, Mathematics, Economics, Engineering, or related field is advantageous but not mandatory.
• 3–6 years of demonstrable experience working as a Data Scientist, Risk Analyst, or Platform Analytics professional.
• Proven experience delivering models or analytics used in real business or production environments.
• Experience in fintech, lending, payments, or transactional data environments is a strong advantage.

Specific Knowledge Requirements

• Statistical modelling and classification techniques.
• Feature engineering and data preparation.
• Model evaluation and explainability concepts.
• Working with large, real-world datasets.
• Applying analytics to product, risk, and operational decisions.