business analyst

data scientist (roads o&m)

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ai expert for plant data model (pdm project) Jobs in Doha

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business intelligence (bi) specialist

data scientist

VAM SYSTEMS

<div>Job Description 
We are currently looking Data Scientist for our Qatar operations with the following terms &amp; conditions.
Core Responsibilities: 
<ul><li>Design, build, and optimize predictive models and machine learning algorithms using structured and semi-structured data.</li><li>
Perform data pre-processing, feature engineering, and model selection independently.</li><li>
Build and maintain automated model pipelines including training, validation, scoring and monitoring.</li><li>
Implement model drift detection, retraining logic, and performance diagnostics.</li><li>
Conduct code-based model explainability (eg. SHAP, LIME), support documentation for governance review.</li></ul>
Expertise Required
<ul><li>Advanced proficiency in Python (Pandas, NumPy, Scikit-leam, XGBoost, LightGBM)</li><li>
Strong command of SQL arid handling large datasets (via warehouse or lake)</li><li>
Experience deploying models using MLflow, Airflow, Docker, or similar tools</li><li>
Familiarity with model performance metrics (ROC AUC, F1, lift/gain, etc.)</li><li>
Hands-on in training and evaluating models for binary classification, multi-class, regression, or time series</li><li>
Exposure to deep learning (PyTorch or Tensorflow) for advanced use cases</li><li>
Working knowledge of embeddings, vector stores, or text-based models</li><li>
Git-based versioning and reproducible ML workflow setup</li></ul>
Joining time frame: 2 weeks (maximum 1 month)</div>

Data Scientist - Banking

Omada Rail Systems

<div><ul><li>Permanent Contract
</li></ul><ul><li>Mid-Senior Level
</li></ul><ul><li>Doha
</li></ul><ul><li>Operations and Maintenance - Operational</li></ul></div>
 <div>About Us 
Egis is a leading global architectural, consulting, construction engineering, operations and mobility services firm. We create and operate intelligent infrastructure and buildings that both respond to the climate emergency and contribute to balanced, sustainable and resilient development. Our 20,500 employees operate across over 100 countries, deploying their expertise to develop and deliver cutting-edge innovations and solutions for clients. Through the wide range of our activities, we are central to the collective organisation of society and the living environment of citizens all over the world.&quot;
With 3,500 employees across 8 countries in the Middle East, Egis has delivered over 700 complex development projects, stimulating economic growth and enhancing quality of life. Ranked among the top ten firms in the Middle East by Engineering News Record (ENR), Egis is committed to sustainable development. The Group&rsquo;s operations in the Middle East are built on strategic acquisitions and a deep understanding of local market conditions. Egis&rsquo; long history of providing comprehensive engineering, consulting, and project management services makes it a trusted partner for regional governments, investors, and developers. 
About the Role 
Collate, combine and manipulate asset and defect data to prioritise works and propose the Capital Investment Plan.
Systems integration and data analysis.
Understanding of data architectures, ability to extract, manipulate, combine data, build dashboards, automation scripts.<ul><li>
Collect, process, and analyze large datasets related to road conditions, traffic patterns, and maintenance activities</li><li>
Develop and implement machine learning models to predict road deterioration and optimize maintenance schedules</li><li>
Create data visualization dashboards and reports to communicate insights to stakeholders effectively</li><li>
Collaborate with engineering teams to integrate data-driven insights into O&amp;M decision-making processes</li><li>
Design and maintain databases to store and manage road infrastructure data</li><li>
Utilize GIS technologies to perform spatial analysis of road networks and related infrastructure</li><li>
Develop predictive models to forecast traffic patterns and identify potential congestion areas</li><li>
Contribute to the development of data-driven strategies for improving road safety and reducing maintenance costs</li><li>
Stay up-to-date with the latest advancements in data science and machine learning techniques relevant to transportation infrastructure</li></ul> 
What's in it for you?</div>

Data Scientist (Roads O&M)

Egis Group

<div>Company Description 
Egis is a leading global architectural, consulting, construction engineering, operations and mobility services firm. We create and operate intelligent infrastructure and buildings that both respond to the climate emergency and contribute to balanced, sustainable and resilient development. Our 20,500 employees operate across over 100 countries, deploying their expertise to develop and deliver cutting-edge innovations and solutions for clients. Through the wide range of our activities, we are central to the collective organisation of society and the living environment of citizens all over the world.&quot;
With 3,500 employees across 8 countries in the Middle East, Egis has delivered over 700 complex development projects, stimulating economic growth and enhancing quality of life. Ranked among the top ten firms in the Middle East by Engineering News Record (ENR), Egis is committed to sustainable development. The Group&rsquo;s operations in the Middle East are built on strategic acquisitions and a deep understanding of local market conditions. Egis&rsquo; long history of providing comprehensive engineering, consulting, and project management services makes it a trusted partner for regional governments, investors, and developers. 
Job Description 
Collate, combine and manipulate asset and defect data to prioritise works and propose the Capital Investment Plan.
Systems integration and data analysis.
Understanding of data architectures, ability to extract, manipulate, combine data, build dashboards, automation scripts.<ul><li>
Collect, process, and analyze large datasets related to road conditions, traffic patterns, and maintenance activities</li><li>
Develop and implement machine learning models to predict road deterioration and optimize maintenance schedules</li><li>
Create data visualization dashboards and reports to communicate insights to stakeholders effectively</li><li>
Collaborate with engineering teams to integrate data-driven insights into O&amp;M decision-making processes</li><li>
Design and maintain databases to store and manage road infrastructure data</li><li>
Utilize GIS technologies to perform spatial analysis of road networks and related infrastructure</li><li>
Develop predictive models to forecast traffic patterns and identify potential congestion areas</li><li>
Contribute to the development of data-driven strategies for improving road safety and reducing maintenance costs</li><li>
Stay up-to-date with the latest advancements in data science and machine learning techniques relevant to transportation infrastructure</li></ul> 

Additional Information</div>

QNB Group

<div>
 
 About QNB
 
 
 
 Established in 1964 as the country&rsquo;s first Qatari-owned commercial bank, QNB Group has steadily grown to become the largest bank in the Middle East and Africa (MEA) region.
 
 
 QNB Group&rsquo;s presence through its subsidiaries and associate companies extends to more than 31 countries across three continents providing a comprehensive range of advanced products and services. The total number of employees is more than 28,000 serving up to 20 million customers operating through 1,000 locations, with an ATM network of 4,300 machines.
 
 
 QNB has maintained its position as one of the highest rated regional banks from leading credit rating agencies including Standard &amp; Poor&rsquo;s (A), Moody&rsquo;s (Aa3) and Fitch (A+). The Bank has also been the recipient of many awards from leading international specialised financial publications.
 
 
 Based on the Group&rsquo;s consistent strong financial performance and its expanding international presence, QNB currently ranks as the most valuable bank brand in the Middle East and Africa, according to Brand Finance Magazine.
 
 
 QNB Group has an active community support program and sponsors various social, educational and sporting events.
 
 
 
 Job Summary
 
 
 
 The data scientist plays a crucial role in driving advanced analytics and AI initiatives across the bank. This role collaborates closely with business and operational leaders to provide expert support, leveraging data analytical and AI tools to deliver strategic insights and actionable recommendations. Through hypothesis-based analysis to machine learning (ML) projects and applying Large Language Models, the data scientist models complex business problems using a variety of techniques including Predictive or prescriptive analytics, deep learning, GenAI and visualization. Additionally, they support senior leadership by overseeing the development of business insights, reports, and AI capabilities to inform decision-making processes and drive impactful outcomes.
 
 
 Main Responsibilities
 
 
 <ul>
 <li>
 Shareholder &amp; Financial: - Implement Key Performance Indicators (KPIs) and monitor the performance of Data &amp; AI-driven solutions to measure their impact on business outcomes. - Promote cost consciousness and efficiency in Data Analytics and AI projects to minimize expenses and maximize returns. - Implement Key Risk Indicators (KRIs) and manage the Group&rsquo;s exposure to Data &amp; AI related risk effectively accordingly. - Coordinate and obtain approval from the EVP Data &amp; Analytics for projects and new systems that would impact capital or operating expenditures. - Take part in the overall QNB Data &amp; Analytics strategy execution. - Communicate comprehensive and cost-effective Data, Analytics &amp; AI solutions to user requirements keeping in mind the Group&rsquo;s budgets and targets while enhancing productivity, to minimise cost, avoid waste, and optimise benefits for the bank. - Promote cost consciousness and efficiency and enhance productivity, to minimise cost, avoid waste, and optimise benefits for the bank. - Act within the limits of the powers delegated to the incumbent.
 </li>
 <li>
 Customer (Internal &amp; External): - Collaborate closely with peers, key divisional stakeholders, and third-party support teams to identify and implement advanced data-driven and AI solutions that deliver substantial value for QNB and its customers. - Translate complex business needs into comprehensive data science and analytics requirements to support strategic business decision-making. - Provide strategic guidance regarding the potential and implementation of AI and data science within the organization. - Assist customers with thorough and insightful responses to their inquiries about the Bank&rsquo;s products and strive to provide innovative solutions to their requests. - Ensure activities are conducted in accordance with Service Level Agreements (SLAs) with internal departments and units to achieve significant improvements in turnaround times. - Build and maintain robust and effective relationships with related departments and units to achieve the Group&rsquo;s overarching objectives. - Provide precise and timely data to external and internal auditors, compliance teams, financial control, and risk management when required. - To assist customers in all their queries on Bank&rsquo;s product and seek solution to their requests. - Maintain activities in accordance with Service Level Agreements (SLAs) with internal departments/units to achieve improvements in turn-around time. - Build and maintain strong/effective relationships with related departments/units to achieve the Group&rsquo;s objectives. - Provide timely/accurate data to external/internal Auditors, Compliance, Financial Control and Risk when required.
 </li>
 <li>
 Internal (Processes, Products, Regulatory):
 </li>
 <li>
 Develop predictive models using a variety of advanced machine learning techniques to extract meaningful insights. - Research and implement cutting-edge techniques and tools in data analytics and artificial intelligence to streamline data analysis processes and enhance decision-making. - Identify and implement new statistical or mathematical methodologies as required for specific data-driven projects. - Build robust data-driven models to address complex business questions and employ large-scale experimentation, analysis, and visualization techniques to generate efficient and repeatable insights. - Integrate domain knowledge into AI solutions, such as leveraging financial risk data, customer journey analytics, quality predictions, and sales and marketing data. - Continuously monitor the performance and health of AI-driven models, ensuring high-quality outputs and efficiency. - Establish best practices for AI development and production infrastructure, including cloud computing, Spark, GPU utilization, and containerization. - Design and conduct analytics with the highest standards of model validation, accuracy, encompassing study design, methodology, algorithms, and statistical modelling.
 </li>
 <li>
 Learning &amp; Knowledge: - Ensure a comprehensive understanding of business requirements to deliver the most effective data, analytics, and AI solutions. - Possess detailed knowledge of system architecture and limitations to determine optimal problem-solving methods. - Understand user requirements and existing data structures to provide appropriate solutions by enhancing existing systems or implementing new ones. - Stay updated with methodologies, tools, and industry trends in machine learning, artificial intelligence, and data analytics. - Proactively identify areas for professional development in data science and undertake relevant development activities. - Seek out opportunities to stay current with advancements in AI and data analytics fields. - Initiate regular meetings within the Application Development department focused on discussing progress, resolving issues, and addressing concerns related to AI and data analytics projects.
 </li>
 <li>
 Legal, Regulatory, and Risk Framework Responsibilities: - Adhere to all pertinent legal, regulatory, and internal compliance requirements, including but not limited to policies on Data Protection, Fraud Control, and Anti-Money Laundering (AML) &amp; Counter-Terrorist Financing (CTF). - Understand and effectively perform your role within the Three Lines of Defence framework to identify, measure, monitor, manage, and report risks related to data, analytics, and AI projects. - Ensure optimal outcomes for clients in line with Conduct Risk policy by leveraging AI and analytics tools ethically and responsibly. - Support the Risk and Control Self-Assessment (RCSA), Key Risk Indicators (KRI), incident reporting, and remediation processes, as applicable, in accordance with Operational Risk Management guidelines specific to data and AI initiatives. - Complete all mandatory training provided by the organization to achieve and maintain the required levels of competence in data science, analytics, and AI fields. - Attend all required (internal and external) seminars and workshops, as instructed, to stay abreast of the latest advancements and best practices in data analytics and AI.
 </li>
 <li>
 Other: - Ensure high standards of data protection and confidentiality to safeguard all data and systems. - Maintaining utmost confidentiality concerning customer data and internal information obtained during the course of business and provide such information on a need-to-know basis only to Senior Management, Audit and Compliance functions, and relevant Regulators. - Maintain high professional standards to uphold the organization's reputation and to strengthen its leadership position in data analytics and AI. - All other ad hoc duties/activities related to data analytics and AI that management might request from time to time
 
 
 </li>
 </ul>
 
 Education And Experience Requirements
 
 
 
 <ul>
 <li>
 University graduate preferably with a Major in computer science, data science, software engineering, information systems, or a related quantitative field.
 </li>
 <li>
 At least 10 years of relevant experience, preferably within a Data Scientist or related role.
 </li>
 <li>
 A PhD in statistics, ML, computer science or the natural sciences, especially physics or any engineering disciplines or equivalent, would be desirable.
 </li>
 <li>
 Candidates must have a specialization in ML, AI, cognitive science, or data science, with extensive hands-on experience in developing data science and AI solutions using machine learning, deep learning, and LLM techniques.
 </li>
 <li>
 Sound knowledge and implementation experience on LLM based solutions.
 </li>
 <li>
 Prior experience in banking is desirable
 </li>
 <li>
 Sound knowledge in cloud technologies ,Python and full stack engineering is must.
 
 
 </li>
 </ul>
 Note: you will be required to attach the following:
 
 
 <ul>
 <li>
 Resume/CV
 </li>
 <li>
 Copy of Passport or QID
 </li>
 <li>
 Copy of Education Certificate
 </li>
 </ul>
</div>

QNB3619 - Assistant Vice President, Data Science

BPO PLUS

We are seeking a detail-oriented Data Analyst with a medical or healthcare background to analyze healthcare data, generate insights, and support data-driven decision-making. The ideal candidate will combine analytical expertise with clinical or medical knowledge to interpret complex healthcare datasets, improve operational efficiency, and support clinical research, patient outcomes, and healthcare performance monitoring.Key Responsibilities<ul><li>Collect, clean, and analyze healthcare datasets from clinical systems, electronic health records (EHR), and operational databases.</li><li>Develop dashboards, reports, and data visualizations to support healthcare management and clinical teams.</li><li>Interpret medical and clinical data to identify trends, patterns, and actionable insights.</li><li>Collaborate with physicians, researchers, and healthcare administrators to understand data requirements.</li><li>Monitor healthcare KPIs such as patient outcomes, treatment effectiveness, and operational efficiency.</li><li>Support clinical research by preparing datasets and performing statistical analysis.</li><li>Ensure compliance with healthcare data privacy regulations and data governance standards.</li><li>Present findings and recommendations to management in a clear and meaningful way.</li><li>Maintain data quality, accuracy, and documentation for all analytical processes.</li></ul>Required Qualifications<ul><li>Bachelor&rsquo;s degree in Medicine, Public Health, Healthcare Management, Health Informatics, Statistics, Data Science, or a related field.</li><li>Medical or clinical background (MBBS, nursing, pharmacy, public health, or related healthcare discipline preferred).</li><li>Proven experience in data analysis within a healthcare or medical environment.</li></ul>Technical Skills<ul><li>Strong knowledge of data analysis tools such as SQL, Excel, Python, R, or SAS.</li><li>Experience with data visualization tools (Power BI, Tableau, or similar).</li><li>Familiarity with Electronic Health Records (EHR) systems and healthcare databases.</li><li>Understanding of healthcare metrics, clinical terminology, and medical datasets.</li><li>Knowledge of statistical analysis methods.</li></ul>Soft Skills<ul><li>Strong analytical and problem-solving skills.</li><li>Ability to translate complex data into clear insights.</li><li>Excellent communication and presentation skills.</li><li>Attention to detail and high level of accuracy.</li><li>Ability to work collaboratively with clinical and non-clinical teams.</li></ul>Preferred Qualifications<ul><li>Experience in clinical research, hospital analytics, or healthcare operations analytics.</li><li>Certification in Data Analytics, Health Informatics, or Biostatistics.</li><li>Experience working with large healthcare datasets or population health data.</li></ul>Job Types: Full-time, Temporary Contract length: 4 monthsPay: QAR16,000.00 - QAR18,000.00 per month

Data Analyst (Medical Background)

Al Adyat consultancy

Job Profile: AI Expert for Plant Data Model (PDM Project)Job SummaryWe are seeking a highly skilled AI Expert to design, build, and operationalize AI/ML solutions that deliver measurable business outcomes. The ideal candidate combines deep machine learning expertise with strong software engineering practices, MLOps proficiency, and stakeholder collaboration. This role will lead end-to-end projects&mdash;from problem framing and data acquisition to model development, deployment, monitoring, and continuous improvement&mdash;while ensuring compliance with security, privacy, and governance policies.Key Responsibilities&middot; Partner with business stakeholders to translate strategic objectives into AI use cases with clear KPIs and ROI.&middot; Own the full ML lifecycle: data sourcing, feature engineering, model selection, training, evaluation, and production deployment.&middot; Implement robust MLOps: CI/CD for ML, model registry, automated testing, reproducible pipelines, and monitoring (drift, performance, fairness).&middot; Design scalable data/feature pipelines using best practices for data quality, lineage, and governance.&middot; Develop models across domains (e.g., predictive analytics, time series, NLP, computer vision, recommender systems).&middot; Optimize models for efficiency and cost across CPU/GPU, leveraging quantization, distillation, or serving optimizations.&middot; Deploy and maintain models on cloud and on-prem platforms; orchestrate with containers and microservices (Docker/Kubernetes).&middot; Establish and enforce responsible AI practices: privacy-preserving methods, bias detection/mitigation, explainability, and secure handling of data.&middot; Produce high-quality documentation: design docs, runbooks, experiment logs, and user guides; present results to technical and non-technical audiences.&middot; Mentor engineers and analysts; contribute to standards, templates, and reusable components within the AI platform.Minimum Qualifications&middot; Bachelor&rsquo;s degree in computer science, Data Science, Engineering, Mathematics, or related field (or equivalent experience).&middot; 5+ years building and deploying ML models in production environments.&middot; Strong programming skills in Python and experience with ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch).&middot; Hands-on experience with data processing frameworks (e.g., Pandas, Spark), SQL/NoSQL databases, and feature stores.&middot; Proficiency in MLOps tooling (e.g., MLflow, DVC, Kubeflow, Airflow) and CI/CD for ML.&middot; Experience deploying to cloud platforms (e.g., Azure Machine Learning, AWS SageMaker, GCP Vertex AI) and container orchestration (Docker/Kubernetes).&middot; Solid understanding of statistics, experiment design (A/B testing), and model validation techniques.&middot; Excellent communication and stakeholder management skills; ability to articulate complex concepts clearly.Preferred Qualifications&middot; Master&rsquo;s or PhD in a quantitative field.&middot; Experience with NLP (transformers, LLMs, RAG) and computer vision (CNNs, detection/segmentation).&middot; Exposure to time-series forecasting, optimization, and reinforcement learning for industrial use cases.&middot; Experience with Azure ecosystem (Azure ML, Databricks, Data Factory, Synapse) and enterprise integration patterns.&middot; Familiarity with privacy-preserving techniques (differential privacy, federated learning) and model interpretability (SHAP, LIME).&middot; Track record of delivering AI solutions in manufacturing, energy, or operations (e.g., predictive maintenance, process optimization).Technical Stack &amp; Tools&middot; Languages: Python (preferred), SQL; familiarity with C++/Java is a plus.&middot; ML/AI: scikit-learn, TensorFlow, PyTorch, XGBoost, Hugging Face; experiment tracking (MLflow).&middot; Data: Pandas, Spark, Delta/Parquet; data versioning (DVC); feature stores.&middot; Pipelines &amp; Orchestration: Airflow, Prefect, Kubeflow; Docker/Kubernetes for serving.&middot; Monitoring &amp; Observability: Prometheus/Grafana; model drift/performance monitoring tools.&middot; DevOps: Git, GitHub/GitLab, CI/CD, testing frameworks (pytest).&middot; Cloud: Azure (preferred), AWS, GCP; serverless and managed ML services.Core Competencies &amp; Soft Skills&middot; Business acumen with the ability to prioritize use cases based on value and feasibility.&middot; Structured problem solving and hypothesis-driven experimentation.&middot; Clear written and verbal communication, storytelling with data, and visualization skills.&middot; Collaboration and mentorship across cross-functional teams (engineering, operations, compliance).&middot; Ownership mindset with a focus on reliability, safety, and continuous improvement.AI Governance, Security &amp; Compliance&middot; Adhere to corporate data governance policies, security standards, and regulatory requirements (e.g., GDPR where applicable).&middot; Implement role-based access controls, encryption, secure secrets management, and audit trails across the ML stack.&middot; Ensure models meet fairness, transparency, and explainability guidelines; conduct regular risk assessments and reviews.Success Criteria &amp; KPIs&middot; Cycle time from ideation to production (weeks).&middot; Model performance against business KPIs (e.g., accuracy, precision/recall, uplift, cost savings).&middot; Operational reliability (SLA adherence, downtime, incident rate).&middot; Adoption and user satisfaction for AI-enabled products and workflows.Compliance metrics (governance checks passed, audits completed).Job Type: Full-timePay: From QAR15,000.00 per monthEducation:<ul><li>Bachelor's (Required)</li></ul>Experience:<ul><li>AI Expert for Plant Data Model (PDM Project) : 3 years (Required)</li></ul>Language:<ul><li>Arabic (Required)</li><li>English (Required)</li></ul>License/Certification:<ul><li>degree in computer science, Data Science (Required)</li></ul>Location:<ul><li>Doha (Required)</li></ul>

AI Expert for Plant Data Model (PDM Project)

PROZ TECHNOLOGIES

JOB DESCRIPTIONThe Risk Data &amp; Modelling Expert oversees end-to-end processing of tax data assets, analyzes cross-system tax information, and develops analytical and predictive models to support compliance risk assessment and strategic decision-making. The role coordinates with IT on analytical tools, reviews international and exchanged tax data, and advises on data governance practices to ensure quality, consistency, and secure access for GTA departments..Key Responsibilities<ul><li>Tax Data Assets &amp; Governance</li><li>Oversee the end-to-end cycle of tax data, including acquisition from internal and external systems, validation, cleansing, storage, and evaluation.</li></ul>Risk Data Analysis &amp; Modelling<ul><li>Analyze tax data using statistical and exploratory techniques to Identify patterns relevant to compliance risk and operational performance.</li><li>Develop and Review predictive and analytical models that Support risk-based assessment, audit targeting, and monitoring of tax gaps.</li></ul>Dashboards, Analytics &amp; Strategic Insights<ul><li>Develop advanced dashboards and analytical views to Support operational monitoring and strategic planning across GTA.</li><li>Prepare structured analytical outputs and reports for senior management, including key indicators, trends, and sectoral insights.</li></ul>Stakeholder Engagement &amp; Requirements<ul><li>Develop and Monitor data governance practices to Ensure tax data quality, consistency, security, and controlled access, including maintenance of central data dictionaries and metadata.</li></ul>SKILLS &amp; ABILITIES<ul><li>Strong vendor and partner management capability in complex, multi-vendor technology or transformation programs.</li><li>Good understanding of technology delivery contracts, service levels, acceptance criteria, and governance mechanisms.</li><li>Ability to manage commercial, delivery, and relationship dimensions in parallel while maintaining strategic focus.</li><li>Strong negotiation, conflict resolution, and performance management skills.</li><li>Ability to interpret technical and operational delivery topics sufficiently to challenge vendors and coordinate with architects/project teams.</li><li>Excellent stakeholder communication and reporting skills, including executive-level summaries and governance materials.</li><li>Coordinate regular meetings with relevant departments to Assess data and analytics needs, Identify gaps, and Align data sources and structures with operational requirements.</li></ul>Tools, Systems &amp; International Data<ul><li>Coordinate with IT to Develop and Enhance analytical tools, including predictive analytics and automated reporting systems that Support tax risk evaluation and decision-making.</li><li>Analyze data received through tax information exchange mechanisms and Evaluate its relevance for risk analysis and compliance activities.</li></ul>Support to Internal &amp; External Requests<ul><li>Advise regulatory and operational units on the use of data and analytical outputs in urgent or high-priority cases requiring rapid information.</li></ul>Review and Prepare analytical inputs for requests from International Cooperation department related to tax data, in line with applicable frameworks.<ul><li>Prepare and Review lists of missing or incomplete tax data needed to Support official tasks and risk analysis.</li></ul>Tools, Systems &amp; International Data<ul><li>Coordinate with IT to Develop and Enhance analytical tools, including predictive analytics and automated reporting systems that Support tax risk evaluation and decision-making.</li></ul>Support to Internal &amp; External Requests<ul><li>Advise regulatory and operational units on the use of data and analytical outputs in urgent or high-priority cases requiring rapid information.</li><li>Review and Prepare analytical inputs for requests from International Cooperation department related to tax data, in line with applicable frameworks.</li><li>Analyze data received through tax information exchange mechanisms and Evaluate its relevance for risk analysis and compliance activities.</li></ul>Preferred Qualifications &amp; Experience<ul><li>Bachelor&rsquo;s degree in Data Science, Statistics, Economics, Computer Science, or related field.</li><li>Master&rsquo;s degree in a related field is preferred.</li><li>Relevant professional certifications are an advantage.</li><li>16+ years of relevant experience in data analytics, statistical modelling, or risk analytics in tax administration, government, or a related financial/compliance environment.</li><li>Demonstrated knowledge of tax data structures, analytical tools, and data governance concepts.</li><li>Strong data analysis and modelling skills using analytical and BI tools.</li><li>Ability to translate complex data into clear insights and decision-support outputs.</li><li>Effective coordination and communication with technical and non-technical stakeholders.</li><li>Attention to data quality, structure, and security.</li></ul>Job Types: Permanent, Contract Contract length: 12 monthsPay: Up to QAR20,000.00 per monthWork Location: In person

Risk Data & Modelling Expert

Explore 336+ Data scientist in Doha this April. Find roles across top companies and apply now to boost your career.

336 data scientist jobs In Doha

Data Scientist - Banking

Data Scientist (Roads O&M)

Data Scientist (Roads O&M)

QNB3619 - Assistant Vice President, Data Science

Data Analyst (Medical Background)

AI Expert for Plant Data Model (PDM Project)

Risk Data & Modelling Expert