Experience & Qualifications Relevant hands-on experience in data science, machine learning, advanced analytics, predictive modelling, statistical analysis or a related role. Bachelor’s degree in data science, computer science, statistics, mathematics, engineering, econometrics or a related quantitative discipline, or equivalent professional experience. A master’s degree or advanced qualification in a relevant quantitative or technical discipline is advantageous. Demonstrated experience developing, evaluating and deploying machine learning or statistical models for practical business use cases. Strong programming skills in Python, R, SQL or other relevant data science and analytics technologies. Experience using machine learning frameworks and libraries such as scikit-learn, PyTorch, TensorFlow, XGBoost or comparable technologies. Strong experience in data exploration, cleaning, feature engineering, model selection, tuning, validation and performance evaluation. Understanding of supervised and unsupervised learning, classification, regression, forecasting, clustering, anomaly detection and optimization techniques. Familiarity with generative AI, large language models, embeddings, retrieval-augmented generation and model evaluation is highly desirable. Experience with cloud data and AI platforms, model development environments, notebooks, version control and data pipelines is preferred. Understanding of model monitoring, drift detection, retraining, model lifecycle management and MLOps practices. Experience evaluating third-party models, vendor solutions or externally sourced analytical capabilities is advantageous. Familiarity with data governance, model risk management, responsible AI, explainability, privacy and bias assessment. Experience within financial services, fintech, payments, fraud, risk, customer analytics or another regulated and data-intensive environment is preferred. Ability to perform with pace, accuracy and sound judgment in a fast-moving, commercially focused environment.
Skills & Competencies Demonstrates strong analytical, statistical and quantitative problem-solving capability. Translates business questions into practical modelling approaches, measurable outcomes and clear evaluation methods. Selects appropriate algorithms and techniques based on the business problem, data characteristics and operational requirements. Understands the strengths, limitations and risks of machine learning, statistical and generative AI models. Applies rigorous experimentation, validation and benchmarking practices rather than relying on model performance in isolation. Identifies data quality, leakage, bias, representativeness and reliability issues early in the development process. Communicates model results, assumptions, limitations and business implications clearly to technical and nontechnical audiences. Uses data, evidence and structured analysis to challenge assumptions and support better decisions. Maintains strong attention to detail across datasets, code, features, experiments, documentation and model outputs. Writes clear, maintainable and reproducible code and applies appropriate version control and development standards. Balances technical sophistication with interpretability, scalability, operational practicality and business value. Collaborates effectively with business stakeholders, solution leads, engineers, data teams, architects and control functions. Plans and prioritizes effectively across multiple models, experiments, datasets and delivery dependencies. Solves problems proactively with a practical, structured and solution-oriented mindset. Takes clear ownership of model quality, data issues, testing results and technical risks. Learns new techniques, platforms and modelling approaches continuously in a rapidly evolving AI environment.
What Success Looks Like in This Role Business and solution requirements are translated into clear, relevant and technically sound data and modelling approaches. Data quality, completeness, bias and readiness issues are identified early enough to avoid preventable delivery delays or unreliable outcomes. Models are developed using appropriate methodologies and meet agreed accuracy, robustness, explainability and performance expectations. Model choices are supported by clear benchmarking against baselines, alternative approaches and relevant third-party solutions. Evaluation methods, testing datasets and performance thresholds are well defined, repeatable and aligned with business outcomes. Model assumptions, limitations, risks and intended use are documented clearly and understood by relevant stakeholders. Approved models integrate effectively into enterprise applications, workflows and production environments. Model performance, data quality and drift are monitored consistently after deployment, with clear triggers for intervention or retraining. Responsible AI, privacy, security, fairness and model governance requirements are embedded throughout the model lifecycle. Reusable data pipelines, evaluation frameworks and modelling components improve the speed and quality of future delivery. Stakeholders view the role as analytically rigorous, commercially aware, collaborative and focused on solving meaningful business problems. Models contribute measurable improvements in revenue, efficiency, forecasting, risk management, customer experience or operational decision-making. The role contributes positively to technical quality, responsible innovation, collaboration and continuous improvement across the AI transformation program.
End-note disclaimer Network International is committed to providing fair and equal opportunities for all candidates and employees. We value inclusion, diversity and equity, and are committed to creating a workplace where everyone is respected, supported and able to contribute to their full potential. All employment decisions are based on merit, business needs and role requirements, without discrimination. |
