AI Data Scientist

Nicfurniture; United Arab Emirates; On-site; AED 350,000 - 650,000; Full time

Job summary

Network International is seeking an experienced AI Data Scientist (Senior Manager) in Dubai to translate business requirements into data and modelling solutions. You will develop, evaluate and improve ML and statistical models, ensuring accuracy, robustness and explainability aligned with business outcomes.

You will collaborate with AI engineers, business teams and risk/compliance functions to prepare data, benchmark options and support models through deployment and monitoring.

Qualifications

• Bachelor’s degree in data science, computer science, statistics, mathematics or related quantitative discipline.
• Master’s degree or advanced qualification in a relevant quantitative/technical field is advantageous.
• Hands-on experience in data science, machine learning, predictive modelling or related role.
• Strong programming skills in Python, R, SQL or other relevant data science technologies.
• Experience using ML frameworks such as scikit-learn, PyTorch, TensorFlow, XGBoost; data exploration and feature engineering.
• Experience with cloud data/AI platforms, notebooks, version control and data pipelines; familiarity with MLOps.
• Experience in financial services, fintech, payments, risk, or data-driven environments is preferred.

Responsibilities

• Translate business requirements and approved designs into data, feature, analytical and modelling requirements.
• Collaborate with stakeholders to define target variables, outcomes and evaluation criteria.
• Explore, clean and curate structured and unstructured data for AI use cases.
• Design and engineer features and datasets for model development and validation.
• Build, train, tune and evaluate ML/statistical models with appropriate techniques.
• Benchmark models against baselines and third-party solutions.
• Define training/validation/testing methodologies and performance thresholds.
• Support deployment, monitoring, drift detection and retraining of models.
• Develop reusable data pipelines and modelling components for faster delivery.
• Provide updates and insights on model performance, data readiness and risks.

Skills

Python

R

SQL

scikit-learn

PyTorch

TensorFlow

XGBoost

MLOps

Data exploration

Model evaluation

Education

Bachelor's degree in data science / related field

Master's degree advantageous

Tools

Python

R

SQL

TensorFlow

PyTorch

scikit-learn

Job description

Job Purpose

Job Description

JOB DESCRIPTION

Job Role

AI Data Scientist

Grade

Senior Manager

Reporting 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, 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.