{"id":2854648,"url":"https://alion.io/job/network-international-ai-data-scientist","title":"AI Data Scientist","company":{"id":67060,"name":"Network International","domain":"network.ae","url":"https://alion.io/company/network","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United Arab Emirates"],"countries":["AE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Embeddings","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"XGBoost","optional":false},{"name":"Interpretability","optional":true}],"status":"live","first_seen_at":"2026-10-19T10:14:51Z","employer_posted_date":"2026-10-19","last_verified_at":"2026-10-28T04:06:12Z","board_verified":true,"closed_at":null,"days_open":9,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":9},"description":"Job Description\nJob Role\nAI Data ScientistGrade\nSenior ManagerReporting to\nAI Project Portfolio Lead\nLocation\nDubai, United Arab EmiratesJob Purpose\nAbout 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.\nOur 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.\nAbout 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.\nWorking 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.\nKey Accountabilities\nTranslate business requirements and approved solution designs into clear data, feature, analytical and modelling requirements.\n\nWork with business stakeholders, AI Champions and solution teams to define target variables, outcomes, evaluation criteria and model success measures.\n\nExplore, analyze, clean and curate structured and unstructured data required for selected AI use cases.\n\nIdentify data quality, completeness, lineage, representativeness, leakage and bias issues, and coordinate remediation with relevant data owners.\n\nDesign and engineer relevant features, variables and datasets for model development, testing and validation.\n\nBuild, train, tune and evaluate machine learning, statistical and predictive models using appropriate techniques and technologies.\n\nBenchmark internally developed models against baseline approaches, third-party models, vendor solutions and alternative analytical methods.\n\nAssess model accuracy, precision, recall, robustness, stability, explainability, fairness, latency and operational suitability.\n\nDefine and maintain appropriate training, validation and testing methodologies, including representative datasets and performance thresholds.\n\nSupport the evaluation and optimization of generative AI and large language model solutions where model selection, retrieval quality, classification or output evaluation is required.\n\nPrepare clear model documentation covering assumptions, methodology, data sources, features, limitations, testing results and intended use.\n\nWork with Applied AI Engineers and Technology teams to integrate approved models into applications, workflows, APIs and production environments.\n\nSupport model deployment, versioning, performance monitoring, drift detection, retraining and continuous improvement.\n\nDefine monitoring metrics and thresholds to identify deterioration in accuracy, data quality, bias, stability or business performance.\n\nApply approved data governance, privacy, security, model risk and responsible AI controls throughout the model lifecycle.\n\nSupport model validation, governance, Information Security, Risk and audit reviews by providing clear evidence, documentation and technical explanations.\n\nDevelop reusable data preparation pipelines, evaluation frameworks, feature engineering components and model development standards.\n\nProvide clear updates, insights and recommendations to the AI Solution Lead and relevant stakeholders on model performance, data readiness, risks and improvement opportunities.\n\nContribute to a collaborative, accountable and performance-driven culture focused on measurable outcomes and responsible use of data and AI.\n\nExperience, Skills & Success Profile\nExperience & Qualifications\nRelevant hands-on experience in data science, machine learning, advanced analytics, predictive modelling, statistical analysis or a related role.\n\nBachelor’s degree in data science, computer science, statistics, mathematics, engineering, econometrics or a related quantitative discipline, or equivalent professional experience.\n\nA master’s degree or advanced qualification in a relevant quantitative or technical discipline is advantageous.\n\nDemonstrated experience developing, evaluating and deploying machine learning or statistical models for practical business use cases.\n\nStrong programming skills in Python, R, SQL or other relevant data science and analytics technologies.\n\nExperience using machine learning frameworks and libraries such as scikit-learn, PyTorch, TensorFlow, XGBoost or comparable technologies.\n\nStrong experience in data exploration, cleaning, feature engineering, model selection, tuning, validation and performance evaluation.\n\nUnderstanding of supervised and unsupervised learning, classification, regression, forecasting, clustering, anomaly detection and optimization techniques.\n\nFamiliarity with generative AI, large language models, embeddings, retrieval-augmented generation and model evaluation is highly desirable.\n\nExperience with cloud data and AI platforms, model development environments, notebooks, version control and data pipelines is preferred.\n\nUnderstanding of model monitoring, drift detection, retraining, model lifecycle management and MLOps practices.\n\nExperience evaluating third-party models, vendor solutions or externally sourced analytical capabilities is advantageous.\n\nFamiliarity with data governance, model risk management, responsible AI, explainability, privacy and bias assessment.\n\nExperience within financial services, fintech, payments, fraud, risk, customer analytics or another regulated and data-intensive environment is preferred.\n\nAbility to perform with pace, accuracy and sound judgment in a fast-moving, commercially focused environment.\n\nSkills & Competencies\nDemonstrates strong analytical, statistical and quantitative problem-solving capability.\n\nTranslates business questions into practical modelling approaches, measurable outcomes and clear evaluation methods.\n\nSelects appropriate algorithms and techniques based on the business problem, data characteristics and operational requirements.\n\nUnderstands the strengths, limitations and risks of machine learning, statistical and generative AI models.\n\nApplies rigorous experimentation, validation and benchmarking practices rather than relying on model performance in isolation.\n\nIdentifies data quality, leakage, bias, representativeness and reliability issues early in the development process.\n\nCommunicates model results, assumptions, limitations and business implications clearly to technical and nontechnical audiences.\n\nUses data, evidence and structured analysis to challenge assumptions and support better decisions.\n\nMaintains strong attention to detail across datasets, code, features, experiments, documentation and model outputs.\n\nWrites clear, maintainable and reproducible code and applies appropriate version control and development standards.\n\nBalances technical sophistication with interpretability, scalability, operational practicality and business value.\n\nCollaborates effectively with business stakeholders, solution leads, engineers, data teams, architects and control functions.\n\nPlans and prioritizes effectively across multiple models, experiments, datasets and delivery dependencies.\n\nSolves problems proactively with a practical, structured and solution-oriented mindset.\n\nTakes clear ownership of model quality, data issues, testing results and technical risks.\n\nLearns new techniques, platforms and modelling approaches continuously in a rapidly evolving AI environment.\n\nWhat Success Looks Like in This Role\nBusiness and solution requirements are translated into clear, relevant and technically sound data and modelling approaches.\n\nData quality, completeness, bias and readiness issues are identified early enough to avoid preventable delivery delays or unreliable outcomes.\n\nModels are developed using appropriate methodologies and meet agreed accuracy, robustness, explainability and performance expectations.\n\nModel choices are supported by clear benchmarking against baselines, alternative approaches and relevant third-party solutions.\n\nEvaluation methods, testing datasets and performance thresholds are well defined, repeatable and aligned with business outcomes.\n\nModel assumptions, limitations, risks and intended use are documented clearly and understood by relevant stakeholders.\n\nApproved models integrate effectively into enterprise applications, workflows and production environments.\n\nModel performance, data quality and drift are monitored consistently after deployment, with clear triggers for intervention or retraining.\n\nResponsible AI, privacy, security, fairness and model governance requirements are embedded throughout the model lifecycle.\n\nReusable data pipelines, evaluation frameworks and modelling components improve the speed and quality of future delivery.\n\nStakeholders view the role as analytically rigorous, commercially aware, collaborative and focused on solving meaningful business problems.\n\nModels contribute measurable improvements in revenue, efficiency, forecasting, risk management, customer experience or operational decision-making.\n\nThe role contributes positively to technical quality, responsible innovation, collaboration and continuous improvement across the AI transformation program.\n\nEnd-note disclaimer\nNetwork 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.","description_format":"text","description_chars":10839,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Cards & Card Issuing","Payment Processing & Gateways"],"lifecycle":[{"event":"open","at":"2026-10-19T14:23:23Z"}],"visa":[],"liveness":{"score":56,"band":"ok","label":"Likely open","p_open":1,"p_active":0.714,"p_room":0.788,"age_days":8,"expected_fill_days":9,"reasons":["conf:1","velocity","win:late"],"computed_at":"2026-10-28T05:59:10Z"},"pay":null,"html_url":"https://alion.io/job/network-international-ai-data-scientist","json_url":"https://alion.io/job/network-international-ai-data-scientist.json","meta":{"generated_at":"2026-10-28T12:59:26Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":17388,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-29T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":67060},"rest":"https://alion.io/mcp/rest/get_company?id=67060"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fnetwork-international-ai-data-scientist"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fnetwork-international-ai-data-scientist"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fnetwork-international-ai-data-scientist"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/network-international-ai-data-scientist\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fnetwork-international-ai-data-scientist"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":2854648}},{"tool":"search_jobs","arguments":{"company":"network.ae","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":2854648},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. Docs: https://alion.io/developers"}}