business analyst

data analyst

data engineer

data scientist

advertising data & insights manager Jobs in Saudi Arabia

advertising data & insights manager

data analytics

Marafiq

<div>
 <p>
  <strong>
   Job Purpose:
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  Provide solutions/innovations towards the implementation of Data Analytics as per best practices in terms of systems configuration to ensure that users have all cutting-edge technology to support them in the achievement of their goals.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Key Duties &amp; Responsibility:
  </strong>
 </p>
 <p>
  1.Work on more than one engagement at a time.
 </p>
 <p>
  2.Assist IT planning team during projects evaluation phase and assist the project manager in the development and implementation of the project plan for a project. This includes the project (scope, deliverables, timelines and resources).
 </p>
 <p>
  3.Gather business requirements and assists in exploring best practices through process design workshops. Works closely with IT planning team to review projects and ensures that the business overall objectives and needs are translated into well-defined and justified projects.
 </p>
 <p>
  4.Be responsible for the execution of assigned project tasks in areas of demonstrated competence. This includes communication with IT and business stakeholders to define the data storage systems, data collection systems, data analytics approaches, processes and other strategies that optimize methodological efficiency, quality, and compliance.
 </p>
 <p>
  5.For assigned area, implement all project activities to meet the requirements by using SAP Activate Methodology.
 </p>
 <p>
  6.Be responsible for business users support through incident and problem management, change management, knowledge transfer and documentation.
 </p>
 <p>
  7.Continually develop core and specific skills and cross-functional expertise and business knowledge. Applies knowledge gained from each project experience to execute future tasks more effectively.
 </p>
 <p>
  8. Data Analysis and Reporting: Analyze large datasets to uncover trends, patterns, and insights that drive business decisions. Present findings in clear, actionable reports and visualizations.
 </p>
 <p>
  9. Advanced Analytics: Utilize advanced statistical models, predictive analytics, and machine learning techniques to support strategic initiatives such as market forecasting, customer segmentation, and competitive analysis.
 </p>
 <p>
  10.Be responsible for timely deliverables with high quality and business satisfaction.
 </p>
 <p>
  11.Train user departmental staff in the use of SAP functionality so that departments can independently utilize the module&rsquo;s functionality without ongoing reliance on SAP technical experts.
 </p>
 <p>
  12.Perform improvements to departmental procedure and handle the implementation of instructions and controls covering a specific area of SAP activity so that all relevant procedural/legislative requirements are fulfilled while delivering a quality, cost
 </p>
 <p>
  -effective service to customers.
 </p>
 <p>
  13.Ensure compliance to all relevant safety, quality and environmental management procedures and controls within a defined area of SAP activity to guarantee employee safety, legislative compliance, delivery of high-quality products/service and a responsible environmental attitude.
 </p>
 <p>
  14.Conduct detailed data analysis on data used across business units to evaluate business process improvement that optimizes efficiency and quality.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Position Requirements:
  </strong>
 </p>
 <p>
  &squf; University degree in Engineering/Finance/Computer Science/ Management.
 </p>
 <p>
  &squf; 4 years SAP related experience, ideally within a large industrial or utilities organization.
 </p>
 <p>
  &squf; 2 years Data Analytics experience required.
 </p>
 <p>
  &squf; Substantial experience of working with users to define business processes and functional specifications.
 </p>
 <p>
  &squf; Experience of training users in system operation.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Professional Certification(s):
  </strong>
  SAP functional certification/accreditation in the relevant SAP module.
 </p>
</div>


Consultant, Data Analytics

Raqmiyat

<div><div><h3 class="jobSectionHeader"><b>Job Information</b></h3>
<ul><div><p>Date Opened</p>
<div><b>04/03/2026</b><b>
</b></div></div><div><p>Job Type</p>
<div><b>Full time</b><b>
</b></div></div><div><p>Job Opening Status</p>
<div><b>In-progress</b><b>
</b></div></div><div><p>Work Experience</p>
<div><b>4-5 years</b><b>
</b></div></div><div><p>Industry</p>
<div><b>Technology</b><b>
</b></div></div><div><p>City</p>
<div><b>Riyadh</b><b>
</b></div></div><div><p>State/Province</p>
<div><b>Ar Riyad</b><b>
</b></div></div><div><p>Country</p>
<div><b>Saudi Arabia</b><b>
</b></div></div><div><p>Zip/Postal Code</p>
<div><b>11564</b><b>
</b></div></div></ul></div><div><h3 class="jobSectionHeader"><b>About Us</b></h3>
<div><div>Raqmiyat is a UAE-based IT and digital transformation company specializing in consulting, staffing, and enterprise technology solutions. We empower banking, government, and enterprise clients across the Middle East to achieve their digital objectives.</div><br>
</div><p></p><h3 class="jobSectionHeader"><b>Job Description</b></h3>
<div><div>Job Overview:<br>
</div><div>
We are looking for a detail-oriented and analytical <b>Data Analytics Engineer</b> with <b>3+ years of experience</b> in data analysis, reporting, and dashboard development. The ideal candidate should have strong hands-on experience in <b>Power BI</b>, data modeling, SQL, and transforming raw data into meaningful business insights to support decision-making.</div><div></div><p><b><br>
Key Responsibilities:</b></p><br>
<ul><li>
Develop, maintain, and optimize <b>interactive dashboards and reports in Power BI</b>.<br>
</li><li>
Gather, analyze, and interpret data from multiple sources to provide actionable insights.<br>
</li><li>
Design and implement <b>data models, DAX measures, and calculated fields</b> in Power BI.<br>
</li><li>
Create <b>ETL / data transformation workflows</b> using Power Query and SQL.<br>
</li><li>
Work closely with business stakeholders to understand reporting requirements and translate them into dashboards.<br>
</li><li>
Ensure data accuracy, consistency, and integrity across reports and systems.<br>
</li><li>
Monitor report performance and improve dashboard usability and visualization standards.<br>
</li><li>
Support ad hoc data analysis requests and generate periodic MIS reports.<br>
</li><li>
Collaborate with cross-functional teams including IT, business, and operations teams.</li></ul><p><b><br>
</b><br>
</p><div></div><br>
<br>
<h3 class="jobSectionHeader"><b>Requirements</b></h3><ul><li>
3+ years of experience in <b>Data Analytics / BI Engineering / Reporting</b><br>
</li><li>
Strong expertise in <b>Power BI (Dashboards, DAX, Power Query, Data Modeling)</b><br>
</li><li>
Good experience in <b>SQL queries, joins, stored procedures, and data extraction</b><br>
</li><li>
Knowledge of <b>Excel (advanced formulas, pivot tables, data analysis)</b><br>
</li><li>
Understanding of <b>ETL processes and data warehousing concepts</b><br>
</li><li>
Strong analytical and problem-solving skills<br>
</li><li>
Good communication and stakeholder management skills</li></ul><p><b><br>
Preferred Skills:</b></p><br>
<ul><li>
Experience with <b>Python / Excel VBA / Tableau</b> is an added advantage<br>
</li><li>
Exposure to <b>Azure / cloud-based data platforms</b> preferred<br>
</li><li>
Knowledge of KPI reporting and business intelligence best practices</li></ul></div></div></div><p></p>

Data Analytics Engineer

MA'ADEN

<div><p><b>Job Purpose
</b></p><ul><li><div>The Lead Specialist, Data Science &amp; Analytics, acts as a technical leader and senior practitioner, driving development, deployment, and scaling of Machine Learning, AI, and advanced analytics solutions across Maaden.
</div></li><li><div>The role ensures analytics products are designed, validated, industrialized, governed, and adopted at scale, providing measurable value across mining, processing, operations, and enterprise functions.
</div></li><li><div>Lead Specialist, Data Science &amp; Analytics is to analyze data, extract insights, and build predictive models that help organizations make smarter decisions and solve difficult problems. By blending expertise in statistics, computer science, and business strategy, they not only analyze complex datasets but also build predictive models that improve operations and shape long-term decisions. With nearly every industry leaning on data today, the demand for skilled professionals continues to grow<br>
</div></li></ul><p></p><p><b>Key Accountabilities:
</b></p><p><b>1. Lead End-to-End Data Science Delivery
</b></p><ul><li>Developing, implementing and maintaining databases and data collection systems
</li><li>Own the full lifecycle of ML/AI initiatives - from problem framing, data exploration, feature engineering, model development, validation, and MLOps handover.
</li><li>Deliver scalable and production-grade models, ensuring alignment with enterprise data governance and AI standards.
</li><li>Performing statistical analysis to understand and interpret data insights
</li><li>Applying data mining techniques to identify patterns, trends, and relationships in large datasets
</li><li>Building predictive models and machine learning algorithms to forecast future outcomes
</li><li>Creating clear data visualizations and reports to communicate findings to stakeholders
</li><li>Working with cross-functional teams to understand business needs and provide data-driven solutions
</li><li>Design and maintain reliable data pipelines and models in partnership with data engineering to ensure data is accurate, timely, and trustworthy for downstream use
</li><li><div>Ensuring data security and compliance with relevant regulations
</div></li><li><div>Drive experimentation, model versioning, automated retraining, and continuous improvement.<br>
</div></li></ul><div></div><p><b>2. Translate Business Needs into AI/Analytics Solutions
</b></p><ul><li><div>Establish frameworks and operating models that make data science accessible, scalable, and embedded within business and technical functions
</div></li><li><div>Engage BU/domain stakeholders to identify value creation opportunities and convert them into actionable analytics use cases.
</div></li><li><div>Build value hypotheses, KPIs, success criteria, and solution roadmaps in collaboration with Data &amp; AI leadership and business teams.<br>
</div></li></ul><p></p><p><b>3. Industrialize AI/ML Models (ML Ops &amp; Architecture)
</b></p><ul><li>Partner with data engineering, data platforms, and cloud/OT architecture teams to embed models into enterprise systems and operational layers.
</li><li>Set standards for production deployment, testing, monitoring, drift handling, and lifecycle governance.
</li><li>Ensure seamless integration of predictive and optimization models into enterprise platforms, control systems, and digital twins
</li>Leverage machine learning, optimization, and computer vision as enabling tools for performance, reliability, and sustainability improvements<br>
<li>
</li></ul><p></p><p><b>4. Responsible AI, Quality &amp; Governance
</b></p><ul><li>Ensure compliance with Maaden&rsquo;s Responsible AI, data quality, and data governance frameworks.
</li><li>Promote reproducibility, documentation, lineage tracking, and auditability across all data science assets.
</li>Ensure transparency, explainability, and continuous model governance across production and enterprise environments<br>
<li>
</li></ul><p></p><p><b>5. Stakeholder Management &amp; Value Realization
</b></p><ul><li>Communicate insights, results, risks, and recommendations to decision-makers using compelling narratives and visualization.
</li>Track value realization, adoption metrics, and operational impact to ensure measurable benefit.<br>
<li>
</li></ul><p></p><p><b>Minimum Qualification, Experience and Core Competencies:
</b></p><p><b>Minimum Qualifications:
</b></p><ul><li><p>Bachelor&rsquo;s degree in computer science, Data Science, Engineering, Mathematics, Statistics, or related fields.<br>
</p></li></ul><p></p><p><b>Minimum Experience:<br>
</b><br>
<br>
</p><ul><li><div><b>Minimum Experience:
</b></div></li><li><div>8 &ndash; 10 years&rsquo; experience in Data Science / Advanced Analytics with industrial, mining, or heavy-asset environments preferred. Including at least 2 years leading or mentoring analytics professionals
</div></li><li><div>Proven ability to translate business problems into analytic approaches: define hypotheses, design analyses, and synthesize results into clear recommendations.
</div></li><li><div>Strong proficiency with modern ML frameworks and cloud platforms (TensorFlow, PyTorch , Azure, AWS) &ndash; Microsoft AI Factory
</div></li><li><div>Strong technical fluency with modern analytics stacks, data modeling, SQL, and experience partnering effectively with engineering teams.<br>
</div></li><li><br>
<b>Machine Learning &amp; Advanced Analytics
</b><ul><li>Hands-on experience developing and deploying <b>machine learning models </b>, including <b>time-series forecasting, predictive modeling, and optimization use cases
</b></li><li>Strong understanding of <b>model performance, validation, stability, and business impact
</b></li></ul></li><li><b>Generative AI &amp; AI Agents
</b><ul><li>Practical experience with <b>Generative AI solutions </b>, including <b>copilots, intelligent automation, and agent-based workflows
</b></li><li>Ability to embed GenAI capabilities into enterprise processes to improve decision-making and operational efficiency
<br>
<br>
Good to Have Capabilities
</li></ul></li><li><b>Data Engineering (IT + OT)
</b><ul><li>Experience designing and maintaining <b>data pipelines </b>across IT and OT environments
</li><li>Exposure to <b>sensor data, streaming / real-time data processing </b>, and industrial data sources
</li><li>Ability to collaborate with data engineering teams to ensure reliable, timely, and trusted data flows
</li></ul></li><li><b>MLOps / AgentOps
</b><ul><li>Experience in <b>model deployment and lifecycle management </b>, including:
<ul><li>Transition from model development to <b>production and scale
</b></li><li>Monitoring, retraining, versioning, and drift management
</li></ul></li><li>Familiarity with automation and operationalization of ML/AI workloads
<br>
<br>
Preferred Experience &amp; Platforms
</li></ul></li><li><b>Cloud &amp; Analytics Platforms
</b><ul><li>Experience working with enterprise cloud platforms, preferably:
<ul><li><b>Microsoft Azure Data Platform
</b></li><li><b>Databricks AI Platform
</b></li><li><b>Microsoft AI Foundry / Microsoft AI Factory
</b></li></ul></li>Understanding of cloud-native architectures for scalable analytics and AI solutions<br>
<li>
</li></ul></li></ul><div></div><p><b>Core Competencies:
</b></p><ul><li><p><b>Model Accuracy &amp; Reliability: </b>Performance, drift stability, and operational uptime.
</p></li><li><p><b>Adoption &amp; Business Impact: </b>Value realized, user adoption, integration success.
</p></li><li><p><b>Delivery Velocity: </b>Timeliness of development cycles and deployment readiness.
</p></li><li><p><b>Compliance &amp; Quality: </b>Alignment with Responsible AI, governance, and documentation standards.</p></li></ul></div><p></p>

Principal Specialist, Data Science & Analytics

<div><div><div><div><div><p><b>Role Purpose:
</b></p><p>The Lead Specialist, Data Science &amp; Analytics, acts as a technical leader and senior practitioner, driving development, deployment, and scaling of Machine Learning, AI, and advanced analytics solutions across Maaden.
</p><p>The role ensures analytics products are designed, validated, industrialized, governed, and adopted at scale, providing measurable value across mining, processing, operations, and enterprise functions.
</p><p>Lead Specialist, Data Science &amp; Analytics is to analyze data, extract insights, and build predictive models that help organizations make smarter decisions and solve difficult problems. By blending expertise in statistics, computer science, and business strategy, they not only analyze complex datasets but also build predictive models that improve operations and shape long-term decisions. With nearly every industry leaning on data today, the demand for skilled professionals continues to grow.
</p><p>1. <b>Lead End-to-End Data Science Delivery
</b></p><ul><li><p>Developing, implementing and maintaining databases and data collection systems
</p></li><li><p>Own the full lifecycle of ML/AI initiatives&mdash;from problem framing, data exploration, feature engineering, model development, validation, and MLOps handover.
</p></li><li><p>Deliver scalable and production-grade models, ensuring alignment with enterprise data governance and AI standards.
</p></li><li>Performing statistical analysis to understand and interpret data insights
</li><li>Applying data mining techniques to identify patterns, trends, and relationships in large datasets
</li><li>Building predictive models and machine learning algorithms to forecast future outcomes
</li><li>Creating clear data visualizations and reports to communicate findings to stakeholders
</li><li>Working with cross-functional teams to understand business needs and provide data-driven solutions
</li><li>Design and maintain reliable data pipelines and models in partnership with data engineering to ensure data is accurate, timely, and trustworthy for downstream use
</li><li>Ensuring data security and compliance with relevant regulations
</li><li>Staying up to date with latest technological trends and techniques in data science
</li><li><p>Drive experimentation, model versioning, automated retraining, and continuous improvement.<br>
</p></li></ul></div></div><br>
<div><div><p><b>2. Translate Business Needs into AI/Analytics Solutions
</b></p><ul><li><p>Establish frameworks and operating models that make data science accessible, scalable, and embedded within business and technical functions
</p></li><li><p>Engage BU/domain stakeholders to identify value creation opportunities and convert them into actionable analytics use cases.
</p></li><li><p>Build value hypotheses, KPIs, success criteria, and solution roadmaps in collaboration with Data &amp; AI leadership and business teams.<br>
</p></li></ul></div></div><br>
<div><div><p><b>3. Industrialize AI/ML Models (ML Ops &amp; Architecture)
</b></p><ul><li><p>Partner with data engineering, data platforms, and cloud/OT architecture teams to embed models into enterprise systems and operational layers.
</p></li><li><p>Set standards for production deployment, testing, monitoring, drift handling, and lifecycle governance.
</p></li><li><p>Ensure seamless integration of predictive and optimization models into enterprise platforms, control systems, and digital twins
</p></li><li><p>Leverage machine learning, optimization, and computer vision as enabling tools for performance, reliability, and sustainability improvements<br>
</p></li></ul></div></div><br>
<div><div><p><b>4. Responsible AI, Quality &amp; Governance
</b></p><ul><li>Ensure compliance with Maaden &rsquo;s Responsible AI, data quality, and data governance frameworks.
</li><li>Promote reproducibility, documentation, lineage tracking, and auditability across all data science assets.
</li>Ensure transparency, explainability, and continuous model governance across production and enterprise environments<br>
<li>
</li></ul></div></div><br>
<div></div><div><div><div><b>Minimum Qualifications:
</b></div><p>Bachelor&rsquo;s degree in computer science, Data Science, Engineering, Mathematics, Statistics, or related fields.<br>
</p><div></div><div><b>Minimum Experience:
</b></div><p>5 &ndash; 8 years&rsquo; experience in Data Science / Advanced Analytics with industrial, mining, or heavy-asset environments preferred. Including at least 2 years leading or mentoring analytics professionals
</p><p>Proven ability to translate business problems into analytic approaches: define hypotheses, design analyses, and synthesize results into clear recommendations.
</p><p>Strong proficiency with modern ML frameworks and cloud platforms (TensorFlow, PyTorch, Azure, AWS)
</p><p>Strong technical fluency with modern analytics stacks, data modeling, SQL, and experience partnering effectively with engineering teams.<br>
</p></div></div><br>
<div><div><div><b>Maaden High-Performance Competencies:
</b></div><p><b>Core Competencies
</b></p><ul><li><p><b>Model Accuracy &amp; Reliability: </b>Performance, drift stability, and operational uptime.
</p></li><li><p><b>Adoption &amp; Business Impact: </b>Value realized, user adoption, integration success.
</p></li><li><p><b>Delivery Velocity: </b>Timeliness of development cycles and deployment readiness.
</p></li><li><p><b>Compliance &amp; Quality: </b>Alignment with Responsible AI, governance, and documentation standards.<br>
</p></li></ul></div></div><br>
<div><div><p><b>Skills:
</b></p><ul><li><b>Machine Learning &amp; AI Leadership
</b></li><li><b>Programming Expertise
</b></li><li><b>Generative AI Proficiency
</b></li><li><b>Strong Analytical &amp; Statistical Skills
</b></li><li><b>Business Strategy &amp; Product Thinking
</b></li><li><b>Ethics, Governance &amp; Responsible AI</b></li></ul></div></div></div></div></div><p></p>

Lead Specialist, Data Science & Analytics II

<div><div><div><div><div><p>Role Purpose:
</p><p>The Data Science &amp; Analytics Manager is responsible for overseeing the end-to-end execution of data science and advanced analytics initiatives that support Maaden&rsquo;s operational and business objectives. The role ensures that data science projects are delivered with high quality, reliability, and measurable impact by managing workflows, coordinating cross-functional stakeholders, and maintaining strong alignment with enterprise data, AI, and governance standards.
</p><p>As an operational leader, the Manager translates business needs into actionable analytical work, supervises the development of models, analytical products, and data pipelines, and ensures their successful deployment and adoption across functions. This role plays a critical part in embedding data-driven decision-making within production, maintenance, supply chain, engineering, and other business units by enabling teams to access trusted insights, robust analytical tools, and fit-for-purpose ML/AI solutions.
</p><p>Through effective project management, quality oversight, and close collaboration with technical experts and business partners, the Data Science &amp; Analytics Manager helps accelerate Maaden&rsquo;s transition to a technology-enabled, AI-informed mining enterprise.<br>
</p><p></p><p><b>1. Manage End‑to‑End Data Science Delivery
</b></p><ul><li><p>Oversee the development, implementation, and maintenance of databases and data collection systems.
</p></li><li><p>Manage the lifecycle of ML/AI initiatives&mdash;supporting problem framing, data exploration, feature engineering, model development, validation, and preparation for MLOps deployment.
</p></li><li><p>Ensure delivery of scalable, production‑ready models in alignment with enterprise data governance and AI standards.
</p></li><li><p>Conduct statistical analysis to interpret data insights and support decision‑making.
</p></li><li><p>Apply data mining techniques to uncover patterns, trends, and relationships in large datasets.
</p></li><li><p>Develop predictive models and machine learning algorithms to forecast future outcomes as required.
</p></li><li><p>Create clear data visualizations, dashboards, and reports to effectively communicate findings to business stakeholders.
</p></li><li><p>Collaborate with cross‑functional teams to understand business needs and translate them into data‑driven solutions.
</p></li><li><p>Ensure seamless integration of predictive and optimization models into enterprise platforms, control systems, and digital twins.
</p></li><li><p>Stay updated on emerging techniques, tools, and technologies in data science and analytics.
</p></li><li><p>Promote experimentation, model versioning, automated retraining, and continuous improvement processes.
</p></li><li><p>Recommend enhancements to existing models and analytics workflows for greater efficiency and impact.<br>
</p></li></ul></div></div><br>
<div><div><p><b>2. Data Operations
</b></p><ul><li><p>Partner with data engineering to design, operate, and maintain reliable data pipelines and analytical models, ensuring data accuracy, timeliness, and trustworthiness.
</p></li><li><p>Monitor data quality, resolve data issues, and enforce data best practices across teams.
</p></li><li><p>Support compliance with data security standards and relevant regulations
</p></li><li><p>Supervise the development and deployment of advanced ML models, predictive analytics, GenAI, and optimization solutions, consistent with responsibilities of analytics leadership roles.
</p></li><li><p>Ensure technical rigor across the end‑to‑end ML lifecycle: scoping data engineering modeling validation MLOps monitoring.
</p></li><li><p>Oversee production reliability, model drift management, and continuous improvement.<br>
</p></li></ul></div></div><br>
<div><div><p><b>3. Team Leadership &amp; Capability Building
</b></p><ul><li><p>Manage and grow a multi‑disciplinary team (Lead Specialists, Senior Specialists, Data Scientists). Roles under this manager are explicitly listed in the org structure.
</p></li><li><p>Build technical excellence through coaching on ML, GenAI, statistics, feature engineering, and experimentation.
</p></li><li><p>Foster a culture of innovation, peer review, and reusable modeling frameworks.<br>
</p></li></ul></div></div><br>
<div><div><p><b>4. AI Governance, Quality &amp; Standards
</b></p><ul><li><p>Steward high standards in model explainability, auditability, lineage, and risk management.
</p></li><li><p>Drive compliance with enterprise data governance, PDPL, and model‑risk principles referenced in internal DS/AI qualification guidance.
</p></li><li><p>Ensure adherence to Responsible AI requirements applicable to DS/AI roles (skills and qualifications guidance).
</p></li><li><p>Enforce data quality standards, lineage, and reproducibility across the team&rsquo;s projects.
</p></li><li><p>Support alignment with enterprise governance frameworks, including PDPL compliance.
</p></li></ul></div></div><br>
</div></div><p><b>Minimum Qualifications, Experience and Competencies
</b></p><p><b>Minimum Qualifications:
</b></p><p>Bachelor&rsquo;s degree in computer science, Engineering, Data Science, Mathematics, Applied Mathematics, or Physics.
</p><p><b>Minimum Experience:
</b></p><ul><li><p>10 &ndash; 12+ years&rsquo; experience in advanced analytics, ML/AI engineering, and industrial data science.
</p></li><li><p>Including at least 4 years leading or mentoring analytics professionals.
</p></li><li><p>Proven ability to translate business problems into analytic approaches: define hypotheses, design analyses, and synthesize results into clear recommendations.
</p></li><li><p>Strong proficiency with modern ML frameworks and cloud platforms (TensorFlow, PyTorch, Azure, AWS).
</p></li><li><p>Strong technical fluency with modern analytics stacks, data modeling, SQL, and experience partnering effectively with engineering teams.
</p></li><li><p>Strong proficiency with modern ML frameworks and cloud platforms (TensorFlow, PyTorch, Azure, AWS).
</p></li><li><p>Proven record of enabling cross-functional business impact through scalable, production-grade data science solutions.
</p></li></ul><p><b>Maaden High-Performance Competencies:
</b></p><p><b>Core Competencies
</b></p><ul><li><p>Value realization from ML/analytics initiatives (impact delivered vs. target).
</p></li><li><p>On‑time delivery of analytics roadmap.
</p></li><li><p>Adoption rate and integration of solutions into business workflows.
</p></li><li><p>Model performance stability (drift incidents, uptime).
</p></li><li><p>Team capability maturity and talent development indicators.<br>
</p></li></ul><p></p><p><b>Skills:
</b></p><ul><li><p>Machine Learning &amp; AI Leadership
</p></li><li><p>Programming Expertise
</p></li><li><p>Generative AI Proficiency
</p></li><li><p>Strong Analytical &amp; Statistical Skills
</p></li><li><p>Business Strategy &amp; Product Thinking
</p></li><li><p>Ethics, Governance &amp; Responsible AI
</p></li><li><p>Leadership &amp; People Management</p></li></ul></div>

Manager, Data Science & Analytics

Abdul Latif Jameel

<div>
 <p>
  <strong>
   Who we are?
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  Abdul Latif Jameel is a diversified business of independent entities that include automotive distribution, auto parts manufacturing, financial services, renewable energy, environmental services, health, land and real estate development, logistics, electronics retailing and media services.
 </p>
 <p>
  Established in 1945 Abdul Latif Jameel has dual headquarters in Jeddah, Saudi Arabia and Dubai, and currently has operations in 30 countries employing approximately 11,000 people from more than 40nationalities.
 </p>
 <p>
  The term Abdul Latif Jameel refers broadly to several distinct, separate and independent legal entities. It is not itself a corporate entity, association or conglomerate run by an overarching parent company but merely refers to a group of distinct and wholly separate legal entities that are collectively referred to as Abdul Latif Jameel.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Main Job Responsibilities:
  </strong>
 </p>
 <p>
  Manage the collection and analysis of CRM data to support business decision-making.
 </p>
 <p>
  Identify trends and insights in customer data to inform marketing and sales strategies.
 </p>
 <p>
  Manage data integration projects to ensure consistency and accessibility of information across departments.
 </p>
 <p>
  Develop and enforce data quality standards to ensure accuracy and reliability of CRM data.
 </p>
 <p>
  Train team members on data analysis tools and techniques to enhance their analytical capabilities.
 </p>
 <p>
  Collaborate with IT department to maintain and upgrade CRM and analytics platforms.
 </p>
 <p>
  Report on data analytics findings and recommend actions to improve CRM effectiveness.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Main Job Qualifications:
  </strong>
 </p>
 <ol>
  <li>
   Bachelor&rsquo;s degree in Business, Analytics, or a related field. A Master&rsquo;s degree or advanced certification in analytics or data science is highly preferred
  </li>
  <li>
   Minimum of 5 years of experience in CRM, analytics, or a related field. Experience in the automotive industry is a significant advantage.
  </li>
  <li>
   Excellent communication skills, with the ability to articulate complex concepts to a non-technical audience.
  </li>
  <li>
   Knowledge of the Saudi Arabian market and consumer behavior is preferred.
  </li>
  <li>
   Proficiency in analytics and CRM software (e.g., Salesforce, Microsoft Dynamics), as well as data visualization tools (e.g., Tableau, Power BI)
  </li>
 </ol>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   The associate will work with a daily standard of (48) hours per week.
  </strong>
 </p>
 <p>
  <strong>
   The job posting will expire in 7 days - Apply while the opportunity lasts!
  </strong>
 </p>
</div>


Data & Performance Analytics Manager

MCG Talent

<div>
 <p>
  <strong>
   We are seeking a data-driven professional to join our client's team as a Data Analyst. Previous experience working in FS/Fintech is crucial in order to be considered.
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  The ideal candidate will possess a strong understanding of data and be able to leverage data analytics to optimize our campaigns and strategies. You will be responsible for collecting, analyzing, and interpreting data to inform decision-making and drive results.
 </p>
 <p>
  <br/>
 </p>
 <p>
  Previous experience working in SG is preferred with trading/brokerage experience a +.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Key Responsibilities:
  </strong>
 </p>
 <ul>
  <li>
   Develop and implement data-driven strategies to measure and analyze campaigns.
  </li>
  <li>
   Collect and analyze data from various sources (social media, media monitoring, website analytics, etc.).
  </li>
  <li>
   Identify key performance indicators (KPIs) and develop reporting dashboards.
  </li>
  <li>
   Provide actionable insights to the team to inform decision-making.
  </li>
  <li>
   Utilize data analytics tools to uncover trends and patterns in audience behavior.
  </li>
  <li>
   Collaborate with cross-functional teams to integrate data insights into overall business strategy.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   The Ideal Candidate:
  </strong>
 </p>
 <ul>
  <li>
   Strong analytical skills and ability to translate data into actionable insights.
  </li>
  <li>
   Experience in using data analytics tools and platforms.
  </li>
  <li>
   Understanding of digital marketing and social media analytics.
  </li>
 </ul>
 <p>
 </p>
</div>


Data Analyst

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