AI Data Scientist
Fa-Euqk-Saasfaprod1 Cx_1 · UAE
Job Description
Job Role
AI Data ScientistGrade
Senior ManagerReporting to
AI Project Portfolio Lead
Location
Dubai, United Arab Emirates
Job Purpose
About Network International: Network International is the largest financial technology company in the Middle East and Africa. Payments is our core business, and we provide services across more than 50 countries, including key markets such as the UAE, Jordan, South Africa and Egypt. In addition to payments, we offer solutions across data and insights, lending, insurance and risk management. Our customers include businesses of every scale and segment, with growing direct-to-consumer card offerings.
Our Employee Value Proposition: At Network International, we always stay ahead. In the fast-paced world of financial services, we thrive on innovation, agility and purposeful collaboration. We invest first in our people, empowering them to make bold decisions, learn fast and develop their expertise alongside industry leaders. Here, solving complex problems means more than using cutting-edge technology; it is about creating meaningful value for our customers together. We foster a culture where trust, accountability and achievement go hand in hand, because success is not just a goal; it is how we work every day, as one team.
About the Role: The AI Data Scientist is responsible for translating business and solution requirements into clear data, feature engineering and modelling approaches for selected AI use cases. The role develops, evaluates and improves machine learning and statistical models, ensuring they are accurate, robust, explainable and aligned with measurable business outcomes.
Working under the direction of the AI Project Portfolio Lead, the role collaborates with Applied AI Engineers, AI Champions, business teams, Data, Technology, Architecture, Risk and Information Security to prepare data, benchmark model options and support models through testing, deployment and ongoing monitoring. The role also helps establish reusable data science practices, evaluation methods and model standards across the AI transformation program.
Key Accountabilities
- Translate business requirements and approved solution designs into clear data, feature, analytical and modelling requirements.
- Work with business stakeholders, AI Champions and solution teams to define target variables, outcomes, evaluation criteria and model success measures.
- Explore, analyze, clean and curate structured and unstructured data required for selected AI use cases.
- Identify data quality, completeness, lineage, representativeness, leakage and bias issues, and coordinate remediation with relevant data owners.
- Design and engineer relevant features, variables and datasets for model development, testing and validation.
- Build, train, tune and evaluate machine learning, statistical and predictive models using appropriate techniques and technologies.
- Benchmark internally developed models against baseline approaches, third-party models, vendor solutions and alternative analytical methods.
- Assess model accuracy, precision, recall, robustness, stability, explainability, fairness, latency and operational suitability.
- Define and maintain appropriate training, validation and testing methodologies, including representative datasets and performance thresholds.
- Support the evaluation and optimization of generative AI and large language model solutions where model selection, retrieval quality, classification or output evaluation is required.
- Prepare clear model documentation covering assumptions, methodology, data sources, features, limitations, testing results and intended use.
- Work with Applied AI Engineers and Technology teams to integrate approved models into applications, workflows, APIs and production environments.
- Support model deployment, versioning, performance monitoring, drift detection, retraining and continuous improvement.
- Define monitoring metrics and thresholds to identify deterioration in accuracy, data quality, bias, stability or business performance.
- Apply approved data governance, privacy, security, model risk and responsible AI controls throughout the model lifecycle.
- Support model validation, governance, Information Security, Risk and audit reviews by providing clear evidence, documentation and technical explanations.
- Develop reusable data preparation pipelines, evaluation frameworks, feature engineering components and model development standards.
- Provide clear updates, insights and recommendations to the AI Solution Lead and relevant stakeholders on model performance, data readiness, risks and improvement opportunities.
- Contribute to a collaborative, accountable and performance-driven culture focused on measurable outcomes and responsible use of data and AI.
Experience, Skills & Success Profile
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, experiment