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Confirmed on the employer's own hiring board on Oct 25, 2026. First seen by Alion on Oct 23, 2026.

Overview
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Headquartered in London, United Kingdom, PayTech Nexus is a specialized recruitment and executive search firm dedicated exclusively to the payments, banking, and financial technology sectors. Operating as the talent acquisition arm of PayTech Group, the company leverages a proprietary matching platform to place pre-vetted interim contractors, agile project squads, and permanent leadership across disciplines such as card issuing, merchant acquiring, and core banking transformation. By combining deep payments domain expertise with AI-assisted, double-blind talent evaluation, it enables global financial institutions, card schemes, and fintech enterprises to rapidly deploy verified specialists for critical digital initiatives.

Job Title: Data Scientist - Digital Payments

Employment type: Contract (On-site)

Contract length: 6 months (with potential extension)

Location: Accra, Ghana (including travel across West and Central Africa)

PURPOSE OF THE PROJECT

The Data Scientist will support the Ghana pilot by turning complex banking and payments data into practical insights, predictive models, and customer segments. The role will work closely with technical and business stakeholders to prepare data, validate analytical approaches, and translate model outputs into actionable implementation recommendations.

Core Responsibilities and Accountabilities:

  • Acquire, profile, clean and transform large transactional, customer and payments datasets for analysis.
  • Develop and validate predictive, classification or propensity models for relevant business and implementation use cases.
  • Build customer segmentation approaches using transactional and behavioural indicators.
  • Perform feature engineering, exploratory analysis and statistical validation, clearly documenting assumptions, and limitations.
  • Create reproducible analytical workflows and communicate findings through concise visualisations and stakeholder-ready summaries.
  • Partner with engineering and integration teams to define reliable data inputs, quality checks, and deployment requirements.
  • Apply appropriate controls when managing sensitive banking and payments data.

Knowledge and Experience:

  • Hands-on experience in data science or advanced analytics within banking, payments, fintech, or a comparable transactional environment.
  • Hands-on experience with Python, SQL, pandas, scikit-learn, Jupyter, Power BI or Tableau, Git, cloud data platforms. Including data wrangling and analysis of large, complex datasets.
  • Practical experience building and validating predictive, classification or segmentation models.
  • Sound understanding of model evaluation, bias, overfitting, data quality, and reproducibility.
  • Ability to explain analytical methods and results clearly to both technical and non-technical.

Mandatory Skills:

  • Bachelor's degree in Finance, Economics, Business Administration, Computer Science, Information Technology, or a related field (Master's preferred).
  • Previous experience within Banking, Digital Payment & Card solutions, or the FinTech industry.
  • Regional experience, including working in a faced-paced matrix organisation.
  • Excellent communication skills in English (written, verbal, and presentation), French is advantageous.
  • Willingness to travel across West and Central Africa may be relevant for subsequent phases.

Preferred Skills:

  • Experience with payment transactions, card portfolios, digital banking, or customer lifecycle analytics.
  • Exposure to model deployment, MLOps, cloud data platforms or dashboarding tools.
  • Experience supporting campaign measurement, experimentation, or customer targeting.

Expected Deliverables:

  • A clear discovery and implementation plan aligned with the assigned client workstream.
  • High-quality technical or analytical outputs, supported by appropriate testing, validation, and documentation.
  • Timely identification and escalation of delivery risks, dependencies and data or integration blockers.
  • Reusable implementation assets, runbooks and structured knowledge transfer to relevant client and delivery teams.
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