18 AI jobs in Qatar

SWATX is seeking a highly skilled and experienced Data Science Manager to lead our growing data science team. In this strategic role, you will be responsible for overseeing the development and implementation of data-driven solutions to solve complex business challenges. You will mentor and guide a team of data scientists, driving innovation and excellence in analytics and machine learning. If you are a strong leader with a passion for data science and a proven track record of delivering impactful solutions, we invite you to join us.

Responsibilities:

Lead and mentor a team of data scientists, providing guidance on best practices in data analysis, machine learning, and statistical modeling Develop and execute the data science strategy aligned with business objectives, ensuring that data-driven insights are integrated into decision-making processes Oversee the design and implementation of innovative data science projects that drive value for the organization Collaborate with cross-functional teams to identify opportunities for leveraging data to improve products, services, and operational efficiency Build and maintain strong relationships with stakeholders, understanding their data needs and ensuring timely delivery of insights Monitor and evaluate the performance of data science models and adjust strategies as necessary to achieve desired results Promote a data-driven culture within the organization by communicating the value of data science initiatives to stakeholders at all levels Stay updated on the latest trends and developments in data science and analytics, and integrate new methodologies and tools as appropriate

Requirements

Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field Proven experience in a data science role, with at least 5+ years of experience, including 2+ years in a managerial or leadership position Strong proficiency in programming languages such as Python, R, and experience with data manipulation and analysis libraries Solid understanding of machine learning algorithms, statistical methodologies, and data modeling techniques Experience with data visualization tools (e.g., Tableau, Power BI) to communicate findings effectively Excellent project management skills and ability to prioritize tasks in a fast-paced environment Strong analytical and problem-solving skills with attention to detail Exceptional communication skills, both verbal and written, in English and Arabic Proven capability to drive collaboration across teams and influence senior stakeholders

Preferable Certificates:

Certified Data Scientist (CDS) Microsoft Certified: Azure Data Scientist Associate Google Cloud Professional Data Engineer

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SWATX is looking for a visionary and results-driven Senior Data Science Manager to join our leadership team. In this pivotal role, you will be responsible for overseeing the strategic direction and execution of data science initiatives across the organization. You will lead a talented team of data scientists, driving innovation in predictive modeling, machine learning, and advanced analytics. As a key contributor to our data strategy, you will collaborate closely with senior leadership and cross-functional teams to deliver impactful insights and solutions that enhance business performance.

Responsibilities:

1. Develop and implement the overall data science vision, strategy, and framework to align with organizational goals.
2. Lead a high-performing team of data scientists, fostering a culture of collaboration, innovation, and continuous learning.
3. Drive the execution of strategic data science projects, ensuring alignment with business objectives and delivery of actionable insights.
4. Collaborate with stakeholders to identify high-impact opportunities for modeling and analytics that can enhance decision-making and operational efficiency.
5. Oversee the design and implementation of complex machine learning algorithms and statistical models to solve business challenges.
6. Monitor industry trends and emerging technologies in data science and analytics, incorporating best practices into the team’s methodologies.
7. Provide mentorship and professional development opportunities for team members to enhance their skill sets and career growth.
8. Present findings and recommendations to senior leadership, translating complex data insights into clear and actionable strategies.

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SWATX is looking for a visionary and results-driven Senior Data Science Manager to join our leadership team. In this pivotal role, you will be responsible for overseeing the strategic direction and execution of data science initiatives across the organization. You will lead a talented team of data scientists, driving innovation in predictive modeling, machine learning, and advanced analytics. As a key contributor to our data strategy, you will collaborate closely with senior leadership and cross-functional teams to deliver impactful insights and solutions that enhance business performance.

Responsibilities: Develop and implement the overall data science vision, strategy, and framework to align with organizational goals. Lead a high-performing team of data scientists, fostering a culture of collaboration, innovation, and continuous learning. Drive the execution of strategic data science projects, ensuring alignment with business objectives and delivery of actionable insights. Collaborate with stakeholders to identify high-impact opportunities for modeling and analytics that can enhance decision-making and operational efficiency. Oversee the design and implementation of complex machine learning algorithms and statistical models to solve business challenges. Monitor industry trends and emerging technologies in data science and analytics, incorporating best practices into the team’s methodologies. Provide mentorship and professional development opportunities for team members to enhance their skill sets and career growth. Present findings and recommendations to senior leadership, translating complex data insights into clear and actionable strategies.

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The Commercial Bank Doha, QatarPosted 16 minutes ago In-Office Permanent ر.ق30k - ر.ق39k
- At Commercial Bank, data is a key driver in strategic decision making; the incumbent is required to have a thorough understanding of data governance, data management principles and have ability to help the bank derive maximum value from the data available. Previous experience with data science and advanced analytical techniques, as well as AI and Machine Learning would be a major success factor. The skills needed for the Head of Data Science role (HoDS) include technical knowledge and problem-solving; the ability to nurture and enhance teams; focus on delivery, stakeholders, strategic thinking and executive presence.

The ability to lead a team of professionals and excellent stakeholder management skills are also required.

The HoDS leads the development of big data capabilities and utilization as well as the coordination of cross-functional analytic initiatives.

**Data Science & Analytics**:

- Overseeing data science initiatives, business intelligence, data management, data analytics and data governance and spearheading data and information strategy including further developing advanced analytics capability.
- The person needs to implement a process-orientated, automated, and collaborative approach to designing, implementing and managing data workflows and a distributed data architecture.
- A good understanding of data science, AI and ML as well as experience in using advanced analytical techniques to solve use cases.

**Delivery of Regulatory Reporting**:

- The role also involves overseeing the delivery of a vast number of regulatory reports and data/information requests including Credit Bureau daily reporting and an array of Qatar Central Bank reports; where timely delivery and accuracy are of utmost importance.

**Required Qualifications**:

- Bachelor’s degree or equivalent experience, Masters/MBA
- Knowledge of Python, R, SQL, AI and Machine Learning

**Required Experience**:

- 5+ years’ experience in Banking/Financial Services
- 15+ years in a data management role
- 5+ year experience with MIS or Regulatory reporting
- 5+ year experience managing small to medium sized teams

About QNB

Established in 1964 as the countrys first Qatari-owned commercial bank, QNB Group has steadily grown to become the largest bank in the Middle East and Africa (MEA) region.

QNB Groups 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 & Poors (A), Moodys (Aa3) and Fitch (A+). The Bank has also been the recipient of many awards from leading international specialised financial publications.

Based on the Groups 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 Senior Vice President (SVP), Data Science, is a strategic leadership role responsible for shaping, leading, and executing QNB&aposs bank-wide Data & AI strategy. This position drives innovation and transformation across the bank using advanced data analytics, machine learning, deep learning, and Generative AI (GenAI). The SVP works closely with executive stakeholders to align data science initiatives with business priorities, ensure strong governance, and build scalable AI capabilities that drive financial performance, operational efficiency, and superior customer experiences. The role also includes oversight of Advanced Analytics, AI model lifecycle management, talent development, and the promotion of ethical Data & AI use across QNB.

Main Responsibilities

• Shareholder & Financial:
• Define and drive execution of QNB&aposs enterprise Data Science & AI strategy aligned to business outcomes.
• Deliver high-impact, AI-powered solutions that generate measurable financial benefits (e.g., revenue growth, cost efficiency, risk mitigation).
• Oversee budgeting and resource allocation across strategic data initiatives.
• Monitor the ROI of Data Science & AI investments using business-focused KPIs and OKRs.
• Establish a data-driven culture across the bank, ensuring AI is embedded in all relevant decision-making processes.
• Implements KPIs and best practices for Senior Vice President, Data Science
• 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 and delegate authority to the respective staff and monitor exercise of the same.
• Demonstrate clear understanding of the important factors behind the bank&aposs financial & non-financial performance.
• Customer (Internal & External):
• Partner with Group business Heads and IT to develop AI solutions that enhance customer engagement, improve
  
  

product personalization, and streamline operations.

• Provide thought leadership on the application of GenAI, advanced analytics, and predictive models for business growth.
• Translate complex data science approaches into business-ready narratives for CXOs and Board-level communication.
• Lead AI capability-building programs for business units, enabling self-serve analytics and citizen data science.
• Champion customer-centric design in all AI and data-driven solutions.
• To assist customers in all their queries on Banks 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 Groups objectives.
• Provide timely/accurate data to external/internal Auditors, Compliance, Financial Control and Risk when required.
• Internal (Processes, Products, Regulatory):
• Establish scalable AI & ML development pipelines including robust model governance, MLOps, and monitoring frameworks.
• Oversee the development of AI models across use cases such as credit risk, client profitability, fraud detection, customer acquisition, and treasury optimization.
• Ensure compliance with internal and external data protection, governance, and regulatory standards.
• Build and institutionalize reusable data products and GenAI agents across business functions.
• Drive innovation in data science practices by integrating cloud-native platforms, LLMs, and enterprise AI tools.
• Continuous Improvement:
• Set examples by leading improvement initiatives through cross-functional teams ensuring successes.
• Identify and encourage people to adopt practices better than the industry standard.
• Continuously encourage and recognise the importance of thinking out-of-the-box within the team.
• Encourage, solicit and reward innovative ideas even in day-to-day issues.
• Learning & Knowledge:
• Lead a high-performing Data Science & AI team, fostering a culture of experimentation, continuous learning, and ethical AI usage.
• Institutionalize knowledge-sharing platforms and AI Centers of Excellence (CoEs).
• Stay abreast of global AI trends, regulatory developments, and advancements in GenAI, LLMs, and deep learning.
• Collaborate with academia, research bodies, and vendors to infuse cutting-edge knowledge into QNB.
• Proactively identify areas for professional development of self and undertake 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.
• Hold meetings with staff and assess their performance and your teams overall performance on a regular basis.
• Take decisive action to ensure speedy resolution of unresolved grievances or conflicts within the team members.
• Identify development opportunities and activities for staff and facilitate/coach them to improve their effectives and prepare them to assume greater responsibilities.
• Legal, Regulatory, and Risk Framework Responsibilities:
• Ensure adherence to all AI-related legal, ethical, and compliance frameworks including AI governance, data privacy, and explainability standards.
• Represent Data Science & AI in Operational Risk, Compliance, and Board-level risk reviews.
• Lead remediation planning for model risk, bias detection, and audit compliance.
• Maintain AI documentation in line with regulatory expectations, particularly for credit, fraud, AML, and client fairness.
• 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.
• 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&aposs 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
  
  

Education And Experience Requirements

• Masters or PhD in Data Science, Computer Science, AI, Engineering, Statistics, or related quantitative field.
• Minimum 15 years of total experience, with at least 10 years in data analytics, data science, or AI leadership roles.
• Proven experience leading AI transformation programs in banking or financial services.
• Deep expertise in AI/ML techniques including LLMs, deep learning, predictive analytics, GenAI, and NLP.
• Strong track record of building and scaling AI teams and delivering enterprise-grade AI solutions.
• Solid knowledge of financial products, customer analytics, credit scoring, risk modeling, and regulatory AI applications.
• Hands-on experience with cloud ecosystems (Azure, AWS, GCP), modern data stacks, and production-grade ML/AI platforms.
• Strong stakeholder management and strategic execution and advisory skills.
• Ability to design AI governance frameworks aligned with regulatory and ethical standards.
• Advanced skills in Python, Spark, SQL, cloud-based ML pipelines, MLOps, and LLM deployment.
• Familiarity with tools like Databricks, Azure AI, Dataiku, and enterprise GenAI platforms.
• Clear communication of complex data science topics to non-technical audiences.
• Demonstrated innovation mindset with bias for action and delivery.
  
  

Note: you will be required to attach the following:

• Resume/CV
• Copy of Passport or QID
• Copy of Education Certificate

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About QNB

Established in 1964 as the countrys first Qatari-owned commercial bank, QNB Group has steadily grown to become the largest bank in the Middle East and Africa (MEA) region.

QNB Groups 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 & Poors (A), Moodys (Aa3) and Fitch (A+). The Bank has also been the recipient of many awards from leading international specialised financial publications.

Based on the Groups 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 Senior Vice President (SVP), Data Science, is a strategic leadership role responsible for shaping, leading, and executing QNB&aposs bank-wide Data & AI strategy. This position drives innovation and transformation across the bank using advanced data analytics, machine learning, deep learning, and Generative AI (GenAI). The SVP works closely with executive stakeholders to align data science initiatives with business priorities, ensure strong governance, and build scalable AI capabilities that drive financial performance, operational efficiency, and superior customer experiences. The role also includes oversight of Advanced Analytics, AI model lifecycle management, talent development, and the promotion of ethical Data & AI use across QNB.

Main Responsibilities

Shareholder & Financial: Define and drive execution of QNB&aposs enterprise Data Science & AI strategy aligned to business outcomes. Deliver high-impact, AI-powered solutions that generate measurable financial benefits (e.g., revenue growth, cost efficiency, risk mitigation). Oversee budgeting and resource allocation across strategic data initiatives. Monitor the ROI of Data Science & AI investments using business-focused KPIs and OKRs. Establish a data-driven culture across the bank, ensuring AI is embedded in all relevant decision-making processes. Implements KPIs and best practices for Senior Vice President, Data Science 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 and delegate authority to the respective staff and monitor exercise of the same. Demonstrate clear understanding of the important factors behind the bank&aposs financial & non-financial performance. Customer (Internal & External): Partner with Group business Heads and IT to develop AI solutions that enhance customer engagement, improve

product personalization, and streamline operations.

Provide thought leadership on the application of GenAI, advanced analytics, and predictive models for business growth. Translate complex data science approaches into business-ready narratives for CXOs and Board-level communication. Lead AI capability-building programs for business units, enabling self-serve analytics and citizen data science. Champion customer-centric design in all AI and data-driven solutions. To assist customers in all their queries on Banks 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 Groups objectives. Provide timely/accurate data to external/internal Auditors, Compliance, Financial Control and Risk when required. Internal (Processes, Products, Regulatory): Establish scalable AI & ML development pipelines including robust model governance, MLOps, and monitoring frameworks. Oversee the development of AI models across use cases such as credit risk, client profitability, fraud detection, customer acquisition, and treasury optimization. Ensure compliance with internal and external data protection, governance, and regulatory standards. Build and institutionalize reusable data products and GenAI agents across business functions. Drive innovation in data science practices by integrating cloud-native platforms, LLMs, and enterprise AI tools. Continuous Improvement: Set examples by leading improvement initiatives through cross-functional teams ensuring successes. Identify and encourage people to adopt practices better than the industry standard. Continuously encourage and recognise the importance of thinking out-of-the-box within the team. Encourage, solicit and reward innovative ideas even in day-to-day issues. Learning & Knowledge: Lead a high-performing Data Science & AI team, fostering a culture of experimentation, continuous learning, and ethical AI usage. Institutionalize knowledge-sharing platforms and AI Centers of Excellence (CoEs). Stay abreast of global AI trends, regulatory developments, and advancements in GenAI, LLMs, and deep learning. Collaborate with academia, research bodies, and vendors to infuse cutting-edge knowledge into QNB. Proactively identify areas for professional development of self and undertake 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. Hold meetings with staff and assess their performance and your teams overall performance on a regular basis. Take decisive action to ensure speedy resolution of unresolved grievances or conflicts within the team members. Identify development opportunities and activities for staff and facilitate/coach them to improve their effectives and prepare them to assume greater responsibilities. Legal, Regulatory, and Risk Framework Responsibilities: Ensure adherence to all AI-related legal, ethical, and compliance frameworks including AI governance, data privacy, and explainability standards. Represent Data Science & AI in Operational Risk, Compliance, and Board-level risk reviews. Lead remediation planning for model risk, bias detection, and audit compliance. Maintain AI documentation in line with regulatory expectations, particularly for credit, fraud, AML, and client fairness. 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. 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&aposs 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

Education And Experience Requirements

Masters or PhD in Data Science, Computer Science, AI, Engineering, Statistics, or related quantitative field. Minimum 15 years of total experience, with at least 10 years in data analytics, data science, or AI leadership roles. Proven experience leading AI transformation programs in banking or financial services. Deep expertise in AI/ML techniques including LLMs, deep learning, predictive analytics, GenAI, and NLP. Strong track record of building and scaling AI teams and delivering enterprise-grade AI solutions. Solid knowledge of financial products, customer analytics, credit scoring, risk modeling, and regulatory AI applications. Hands-on experience with cloud ecosystems (Azure, AWS, GCP), modern data stacks, and production-grade ML/AI platforms. Strong stakeholder management and strategic execution and advisory skills. Ability to design AI governance frameworks aligned with regulatory and ethical standards. Advanced skills in Python, Spark, SQL, cloud-based ML pipelines, MLOps, and LLM deployment. Familiarity with tools like Databricks, Azure AI, Dataiku, and enterprise GenAI platforms. Clear communication of complex data science topics to non-technical audiences. Demonstrated innovation mindset with bias for action and delivery.

Note: you will be required to attach the following:

Resume/CV Copy of Passport or QID Copy of Education Certificate Show more

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Job Summary

You will be responsible for end-to-end data science cycles, encompassing designing, training, implementing, evaluating, and monitoring machine learning models, and will also design and implement highly scalable tools and algorithms based on state-of-the-art Machine Learning and Deep Learning methodologies.

You will work across the ML stack, from researching models, working with large datasets, training, and tuning existing models to creating new models, deploying them at scale, analyzing results, and presenting findings to stakeholders across tech and business domains.

Reporting Structure

• This job reports to the Manager – AI.

Job Objectives

• Develop and implement advanced predictive models to optimize customer experiences and other business outcomes.

• Analyze large and complex datasets to extract actionable insights and drive business decisions.

• Interpret results and provide actionable insights to guide real-time decision-making within the business context.

• Collaborate with cross-functional teams to ensure proper deployment and integration of ML models for new releases.

Job Responsibilities

Predictive Modeling and Deployment

• Develop and implement advanced predictive models to forecast key business metrics such as sales, customer churn, or product demand.

• Utilize predictive modeling to optimize customer experiences and other business outcomes.

• Execute machine learning models, algorithms, and statistical techniques to analyze historical data and ensure scalability and efficiency.

Data Preparation and Analysis

• Develop and use advanced software programs, algorithms, and query techniques to cleanse, integrate, and evaluate datasets for model inputs.

• Analyze large and complex datasets to extract actionable insights and identify trends and patterns that can drive business decisions.

• Identify manual human processes, understand user behaviors, and analyze use cases that can be augmented or automated.

Model Deployment and Interpretation

• Deploy models into production environments and monitor their performance over time.

• Apply statistical, mathematical, and predictive modeling techniques to build, maintain, and improve real-time decision systems.

• Interpret results, develop insights within the business context, and provide guidance on risks and limitations.

Development & Documentation

• Write the code as per agreed software design rules to keep it aligned with the rest of the code base.

• Code the final implementation that the generated code is referring to.

• Follow company software data protection and security guidelines in developing software.

• Accurately estimate the time needed to complete an assigned task.

• Identify possible causes of issues or problems.

• Think through and recommend solutions when raising issues around code, requirements, etc.

• Write technical design documentation that fully defines all application code.

• Maintain detailed knowledge of iHorizons products and services.

• Understand the business impact for labs outcomes.

Collaboration & Team Guidance

• Stay updated on the latest research, learn new applications, tools, and technologies in the fields of data science and machine learning through intensive and focused effort.

• Collaborate with technical and non-technical business partners to develop analytical dashboards describing ML algorithm findings to stakeholders.

• Collaborate with other teams to perform code reviews and oversee proper deployment for new releases.

• Actively mentor and support mid-level and junior developers in their professional growth.

• Provide guidance on best practices in machine learning, code reviews, and project design.

• Conduct regular knowledge-sharing sessions, workshops, and one-on-one coaching to enhance the technical skills and problem-solving abilities of less experienced team members.

• Foster an inclusive and collaborative environment that encourages continuous learning and development within the team.

Job Requirements

Educational Qualification

• Bachelor's degree in data science, statistics, and computer science is a MUST.

Previous Work Experience

• 6+ Years of experience in data science or machine learning.

• Must have strong experience in at least one of the following areas:

• Vision models

• NLP models (Experience in Arabic NLP is a huge plus)

Skills and Abilities

• Proficient in python, TensorFlow, keras and pytorch.

• Good experience in:

• SQL and non-relational databases.

• Data analytics reports generation.

• ML model development deployment.

About iHorizons

iHorizons is a leading provider of business solutions and technology services in the Arab World. Headquartered in Doha, Qatar, we work with prominent clients to support their digital service migration journeys. The ultimate outcomes are radically improved customer experiences and increased operational efficiencies.

We are a forward-looking organization, always enhancing our methodologies and adopting new technologies so that we would serve our customers better and improve our position in the market. We have an outstanding culture, and we provide unique opportunities for career growth to all our staff.

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Job Summary You will be responsible for end-to-end data science cycles, encompassing designing, training, implementing, evaluating, and monitoring machine learning models, and will also design and implement highly scalable tools and algorithms based on state-of-the-art Machine Learning and Deep Learning methodologies. You will work across the ML stack, from researching models, working with large datasets, training, and tuning existing models to creating new models, deploying them at scale, analyzing results, and presenting findings to stakeholders across tech and business domains. Reporting Structure This job reports to the Manager – AI.

Job Objectives Develop and implement advanced predictive models to optimize customer experiences and other business outcomes.

Analyze large and complex datasets to extract actionable insights and drive business decisions.

Interpret results and provide actionable insights to guide real-time decision-making within the business context.

Collaborate with cross-functional teams to ensure proper deployment and integration of ML models for new releases.

Job Responsibilities Predictive Modeling and Deployment Develop and implement advanced predictive models to forecast key business metrics such as sales, customer churn, or product demand.

Utilize predictive modeling to optimize customer experiences and other business outcomes.

Execute machine learning models, algorithms, and statistical techniques to analyze historical data and ensure scalability and efficiency.

Data Preparation and Analysis Develop and use advanced software programs, algorithms, and query techniques to cleanse, integrate, and evaluate datasets for model inputs.

Analyze large and complex datasets to extract actionable insights and identify trends and patterns that can drive business decisions.

Identify manual human processes, understand user behaviors, and analyze use cases that can be augmented or automated.

Model Deployment and Interpretation Deploy models into production environments and monitor their performance over time.

Apply statistical, mathematical, and predictive modeling techniques to build, maintain, and improve real-time decision systems.

Interpret results, develop insights within the business context, and provide guidance on risks and limitations.

Development & Documentation Write the code as per agreed software design rules to keep it aligned with the rest of the code base.

Code the final implementation that the generated code is referring to.

Follow company software data protection and security guidelines in developing software.

Accurately estimate the time needed to complete an assigned task.

Identify possible causes of issues or problems.

Think through and recommend solutions when raising issues around code, requirements, etc.

Write technical design documentation that fully defines all application code.

Maintain detailed knowledge of iHorizons products and services.

Understand the business impact for labs outcomes.

Collaboration & Team Guidance Stay updated on the latest research, learn new applications, tools, and technologies in the fields of data science and machine learning through intensive and focused effort.

Collaborate with technical and non-technical business partners to develop analytical dashboards describing ML algorithm findings to stakeholders.

Collaborate with other teams to perform code reviews and oversee proper deployment for new releases.

Actively mentor and support mid-level and junior developers in their professional growth.

Provide guidance on best practices in machine learning, code reviews, and project design.

Conduct regular knowledge-sharing sessions, workshops, and one-on-one coaching to enhance the technical skills and problem-solving abilities of less experienced team members.

Foster an inclusive and collaborative environment that encourages continuous learning and development within the team.

Job Requirements Educational Qualification Bachelor's degree in data science, statistics, and computer science is a MUST.

Previous Work Experience 6+ Years of experience in data science or machine learning.

Must have strong experience in at least one of the following areas:

Vision models

NLP models (Experience in Arabic NLP is a huge plus)

Skills and Abilities Proficient in python, TensorFlow, keras and pytorch.

Good experience in:

SQL and non-relational databases.

Data analytics reports generation.

ML model development deployment.

About iHorizons iHorizons is a leading provider of business solutions and technology services in the Arab World. Headquartered in Doha, Qatar, we work with prominent clients to support their digital service migration journeys. The ultimate outcomes are radically improved customer experiences and increased operational efficiencies. We are a forward-looking organization, always enhancing our methodologies and adopting new technologies so that we would serve our customers better and improve our position in the market. We have an outstanding culture, and we provide unique opportunities for career growth to all our staff.

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Oryx Universal College in partnership with Liverpool John Moores University | Full time

Artifical Intelligence & Machine Learning Engineer

We are seeking a highly skilled and innovative Artificial Intelligence and Machine Learning Engineer to join our team. The ideal candidate will be responsible for designing, developing, and deploying AI and ML models that drive impactful solutions across various domains, including education, data analytics, automation, and smart systems. You will work alongside interdisciplinary teams to integrate cutting-edge technologies into practical applications, transforming data into intelligent action.

Key Responsibilities:

• Design, train, evaluate, and deploy machine learning and deep learning models
  
• Build and maintain scalable AI solutions for real-time or batch processing systems.
  
• Optimise model performance, including accuracy, latency, and resource consumption.
  
• Collaborate with data engineers and analysts to source, clean, and transform data.
  
• Apply advanced statistical and data mining techniques to extract meaningful patterns.
  
• Stay updated with the latest advancements in AI and ML technologies.
  
• Prototype innovative solutions, contribute to research publications, and recommend adoption of new frameworks.
  
• Systems Integration
  
• Develop APIs and services to integrate AI solutions into existing platforms or applications.
  
• Collaborate with software engineers and DevOps teams to ensure production-level stability.
  
• Work closely with academic and operational teams to understand business needs and translate them into technical solutions.
  
• Prepare documentation, reports, and presentation materials to communicate findings and methodologies.
  

Requirements Required Qualifications:

Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field (Master’s or PhD preferred).

Minimum 3 years of hands-on experience in AI/ML engineering or data science roles.

Proficient in Python and common ML libraries (e.g., TensorFlow, PyTorch, scikit-learn, Keras).

Strong understanding of machine learning algorithms, neural networks, natural language processing (NLP), and computer vision.

Experience in model deployment using cloud platforms (e.g., AWS, Azure, GCP) or containerised environments (Docker, Kubernetes).

Knowledge of MLOps tools and CI/CD pipelines for ML projects.

Familiarity with big data platforms (e.g., Spark, Hadoop).

Experience with academic or educational systems (LMS, AI in education) is a plus.

Demonstrated contributions to open-source projects, research publications, or hackathons.

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