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research associate ai data science forecast specialist

University of Doha for Science & Technology

<div>
 
 Overview
 
 
 
 University of Doha for Science and Technology (UDST) is the first national applied University in the State of Qatar, offering applied bachelor&rsquo;s and master&rsquo;s degrees in addition to certificates and diplomas in various fields. UDST has over 50 programs in the fields of Engineering Technology and Industrial Trades, Business Management, Computing and Information Technology, Health Sciences, Continuing and Professional Education and more.
 
 
 With more than 700 staff and over 8,000 students, UDST is the destination for top-notch applied and experiential learning. The University is recognized for its student-centred learning and state-of-the-art facilities. Our faculty are committed to delivering pedagogically-sound learning experiences with the incorporation of innovative technological interventions, to further enhance students&rsquo; skills and help develop talented graduates that can effectively contribute to a knowledge-based economy and make Qatar&rsquo;s National Vision 2030 a reality.
 
 
 We are seeking a highly motivated Research Associate to join a collaborative research project between the University of Doha for Science and Technology (UDST) and a leading technology-driven business in Qatar. The ideal candidate will have a strong background in data engineering, machine learning, forecasting, and MLOps, with experience in building scalable data infrastructure and deploying predictive models into production environments.
 
 
 
 Responsibilities
 
 
 
 
 Key Responsibilities:
 
 
 
 <ul>
 <li>
 Data Infrastructure &amp; Collection
 </li>
 <li>
 Design and implement ETL pipelines for data ingestion and transformation.
 </li>
 <li>
 Perform data audits, implement quality checks, and maintain comprehensive data documentation.
 </li>
 <li>
 Forecasting Module Development
 </li>
 <li>
 Develop and benchmark advanced time-series forecasting models.
 </li>
 <li>
 Train and fine-tune models with scalable frameworks, ensuring performance against business KPIs.
 </li>
 <li>
 Prototype, validate, and document predictive models.
 </li>
 <li>
 Microservices, MLOps &amp; Deployment
 </li>
 <li>
 Integrate forecasting modules with feature stores and existing data pipelines.
 </li>
 <li>
 Containerize models using Docker and set up CI/CD pipelines for deployment.
 </li>
 <li>
 Implement monitoring dashboards, drift detection, and alerts to ensure reliable system performance.
 </li>
 <li>
 Documentation &amp; Knowledge Transfer
 </li>
 <li>
 Prepare full technical documentation, including API specifications and user guides.
 </li>
 <li>
 Develop training materials, workshops, and modules for knowledge transfer.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Educational Background:
 </li>
 <li>
 Ph.D. in Computer Science, Data Engineering, Machine Learning, or a related field, OR
 </li>
 <li>
 Master&rsquo;s degree in Computer Science/Engineering with at least 3 years of industry experience in Data Infrastructure, Forecasting, and MLOps.
 </li>
 <li>
 Technical Expertise:
 </li>
 <li>
 Strong knowledge of time-series forecasting, machine learning, and predictive analytics.
 </li>
 <li>
 Proficiency in ETL pipelines, data quality assurance, and integration of external datasets.
 </li>
 <li>
 Hands-on experience with MLOps tools (Docker, CI/CD, monitoring, drift detection).
 </li>
 <li>
 Familiarity with cloud-based environments (AWS, GCP, or Azure) is highly desirable.
 </li>
 <li>
 Programming Skills: Strong programming skills in languages such as Python or R. Familiarity with machine learning libraries and frameworks (e.g., Scikit-Learn, TensorFlow, PyTorch) is a must.
 </li>
 <li>
 Analytical Skills: Demonstrated ability to analyze complex datasets and develop data-driven solutions.
 </li>
 <li>
 Communication Skills: Excellent written and verbal communication skills, with the ability to convey complex technical concepts to non-technical audiences.
 </li>
 <li>
 Team Collaboration: Proven experience working in collaborative team environments and managing multiple tasks effectively.
 </li>
 </ul>
</div>

Research Associate -AI-Data Science & Forecast Specialist

University of Doha for Science and Technology

<div>Overview:

University of Doha for Science and Technology (UDST) is the first national applied University in the State of Qatar, offering applied bachelor&rsquo;s and master&rsquo;s degrees in addition to certificates and diplomas in various fields. UDST has over 50 programs in the fields of Engineering Technology and Industrial Trades, Business Management, Computing and Information Technology, Health Sciences, Continuing and Professional Education and more.
With more than 700 staff and over 8,000 students, UDST is the destination for top-notch applied and experiential learning. The University is recognized for its student-centred learning and state-of-the-art facilities. Our faculty are committed to delivering pedagogically-sound learning experiences with the incorporation of innovative technological interventions, to further enhance students&rsquo; skills and help develop talented graduates that can effectively contribute to a knowledge-based economy and make Qatar&rsquo;s National Vision 2030 a reality.
We are seeking a highly motivated Research Associate to join a collaborative research project between the University of Doha for Science and Technology (UDST) and a leading technology-driven business in Qatar. The ideal candidate will have a strong background in data engineering, machine learning, forecasting, and MLOps, with experience in building scalable data infrastructure and deploying predictive models into production environments.
Responsibilities:

Key Responsibilities:
1. Data Infrastructure &amp; Collection<ul><li>
Design and implement ETL pipelines for data ingestion and transformation.</li><li>
Perform data audits, implement quality checks, and maintain comprehensive data documentation.</li></ul>
2. Forecasting Module Development<ul><li>
Develop and benchmark advanced time-series forecasting models.</li><li>
Train and fine-tune models with scalable frameworks, ensuring performance against business KPIs.</li><li>
Prototype, validate, and document predictive models.</li></ul>
3. Microservices, MLOps &amp; Deployment<ul><li>
Integrate forecasting modules with feature stores and existing data pipelines.</li><li>
Containerize models using Docker and set up CI/CD pipelines for deployment.</li><li>
Implement monitoring dashboards, drift detection, and alerts to ensure reliable system performance.</li></ul>
4. Documentation &amp; Knowledge Transfer<ul><li>
Prepare full technical documentation, including API specifications and user guides.</li><li>
Develop training materials, workshops, and modules for knowledge transfer.</li></ul>
Qualifications:
<ul><li>Educational Background:</li><li><ul><li>
Ph.D. in Computer Science, Data Engineering, Machine Learning, or a related field, OR</li><li>
Master&rsquo;s degree in Computer Science/Engineering with at least 3 years of industry experience in Data Infrastructure, Forecasting, and MLOps.</li></ul></li><li>
Technical Expertise:</li><li>
</li><li><ul><li>
Strong knowledge of time-series forecasting, machine learning, and predictive analytics.</li><li>
Proficiency in ETL pipelines, data quality assurance, and integration of external datasets.</li><li>
Hands-on experience with MLOps tools (Docker, CI/CD, monitoring, drift detection).</li><li>
Familiarity with cloud-based environments (AWS, GCP, or Azure) is highly desirable.</li></ul></li></ul><ul><li>
Programming Skills: Strong programming skills in languages such as Python or R. Familiarity with machine learning libraries and frameworks (e.g., Scikit-Learn, TensorFlow, PyTorch) is a must.</li><li>
Analytical Skills: Demonstrated ability to analyze complex datasets and develop data-driven solutions.</li><li>
Communication Skills: Excellent written and verbal communication skills, with the ability to convey complex technical concepts to non-technical audiences.</li><li>
Team Collaboration: Proven experience working in collaborative team environments and managing multiple tasks effectively.</li></ul></div>

<div>Overview:

University of Doha for Science and Technology (UDST), officially established by Emiri Decision No. 13 of 2022, is Qatar&rsquo;s first national applied university and the country&rsquo;s premier destination for academic, technical, and professional education. With more than 8,000 students and 700 staff, UDST offers over 70 applied bachelor&rsquo;s, master&rsquo;s, diploma, and certificate programs across its five colleges: Business, Computing &amp; Information Technology, Engineering &amp; Technology, Health Sciences, and General Education. In addition, UDST houses specialized training centers that serve both individuals and industry.
UDST is recognized for its student-centered learning, cutting-edge facilities, and applied, experiential approach. Our world-class faculty and researchers leverage innovative learning technologies and foster partnerships with global institutions to develop highly skilled graduates who contribute directly to Qatar&rsquo;s knowledge-based economy and support the goals of Qatar National Vision 2030. As a growing hub for research and innovation, UDST is home to advanced projects that bridge academia and industry.
We are seeking a highly motivated Research Associate to join a collaborative project between University of Doha for Science and Technology (UDST) and Qatar Airways. The ideal candidate will have a strong background in data analysis, artificial intelligence, operations research, and optimization techniques, with a particular emphasis on Data-driven approaches.
Responsibilities:
<ul><li>Data Preparation and Analysis: Collect, preprocess, and analyze large datasets related to flight operations and other pertinent areas to inform model development and validation. This includes handling missing or incorrect data, identifying patterns, and ensuring data quality.</li><li>
Model Development: Design and implement predictive models to forecast crew requirements based on historical and real-time operational data. Account for uncertainty due to disruptions and unforeseen events.</li><li>
Optimization Algorithms: Develop and apply operations research methodologies and reinforcement learning algorithms to dynamically generate optimal solutions that enhance operational efficiency and adhere to all relevant constraints.</li><li>
Simulation and Testing: Create simulation environments to test and validate the performance of developed models and algorithms under various operational scenarios and disruptions.</li><li>
Regulatory Compliance Integration: Ensure that all models and optimization workflows comply with aviation regulations, including duty-time limits, rest periods, qualification rules, and safety standards. Collaborate with compliance officers to embed these rules effectively in the system logic.</li><li>
Collaboration: Work closely with interdisciplinary teams, including data scientists, operations researchers, and airline operations personnel, to ensure the practical applicability of developed solutions.</li><li>
Documentation and Reporting: Prepare comprehensive documentation of methodologies, algorithms, and findings. Present research outcomes to stakeholders and contribute to academic publications</li></ul>
Qualifications:

Qualifications:<ul><li>
Educational Background: a Ph.D. Computer, Operations Research, Applied Mathematics, or a related field OR a Master degree in Computer science with a minimum of 5 years of experience in projects involving Data Science, Optimization and AI.</li><li>
Technical Expertise: Proficiency in data analysis, machine learning, and operations research techniques. Experience with predictive modeling and optimization algorithms such as Meta-heuristics is essential. Mastery of reinforcement learning (RL) is a plus.</li><li>
Programming Skills: Strong programming skills in languages such as Python or Rust or R. Familiarity with machine learning libraries and frameworks (e.g., Scikit-Learn, TensorFlow, PyTorch) is a must.</li><li>
Analytical Skills: Demonstrated ability to analyze complex datasets and develop data-driven solutions.</li><li>
Communication Skills: Excellent written and verbal communication skills, with the ability to convey complex technical concepts to non-technical audiences.</li><li>
Team Collaboration: Proven experience working in collaborative team environments and managing multiple tasks effectively.</li></ul>
Preferred Qualifications:<ul><li>
Experience in airline operations, or related domains.</li><li>
Familiarity with regulatory frameworks governing aviation operations.</li></ul></div>

Research Associate – AI & Data Science Specialist for Airline Operations Optimization

Laiwyer.ai

<div>
 
 
 Company Description
 
 
 
 
 
 Laiwyer.ai is an AI-powered legal assistant designed to revolutionize legal research for professionals in the Middle East and North Africa. The platform delivers clear, accurate answers to complex legal questions in both Arabic and English, streamlining workflows and reducing research costs. Laiwyer.ai provides access to official legal databases across Qatar, the UAE, Saudi Arabia, and Egypt, enabling seamless cross-border research and bilingual analysis. Trusted by law firms, corporate legal departments, and government agencies, Laiwyer.ai offers advanced tools like real-time legal updates, cross-jurisdiction analysis, and AI-driven insights to optimize legal operations. Learn more at www.Laiwyer.ai.
 
 
 
 
 
 
 Role Description
 
 
 
 
 
 This is a full-time remote role for a B2B Performance Marketing Specialist. The specialist will be responsible for developing and executing data-driven marketing campaigns to drive lead generation and customer acquisition. Daily tasks include market research, analyzing campaign performance, designing marketing strategies, optimizing campaigns, and collaborating with the sales team to meet business objectives. The role also involves maintaining strong communication with stakeholders and providing exceptional support to meet business goals.
 
 
 
 
 
 
 Qualifications
 
 
 
 
 <ul>
 <li>
 Strong Marketing Strategy and Market Research abilities with a focus on B2B performance marketing
 </li>
 <li>
 Understanding of Sales processes and techniques to align marketing efforts with revenue goals
 </li>
 <li>
 Analytical and data-driven mindset to evaluate campaign performance
 </li>
 <li>
 Experience with digital advertising platforms and analytics tools is advantageous
 </li>
 <li>
 Proficiency in English is required, and fluency in Arabic is a strong plus
 </li>
 <li>
 Self-motivated with the ability to work independently in a remote environment
 </li>
 </ul>
</div>

Marketing Specialist

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

Qatar National Bank

<div>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 
A. 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'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'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.
B. 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'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'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'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's objectives. - Provide timely/accurate data to external/internal Auditors, Compliance, Financial Control and Risk when required.
C. Internal (Processes, Products, Regulatory):
- 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.
D. 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.
E. 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.
F. 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
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>
Required Special Skills: 
- Advanced oral and written communication skills in English. - Strong interpersonal and presentation abilities to convey complex data and AI concepts.
- Comprehensive understanding of relevant laws, regulations, and practices related to data and AI. - Capability to make informed decisions and implement data and AI initiatives effectively. - Personal integrity and efficient self-management in handling data and AI projects. - Proficient in planning, organizing, and analyzing tasks. - Results-oriented with a keen focus on achieving objectives. - Exceptional analytical skills to interpret data and AI results and communicate findings to all levels of management.
- Expertise in designing, building, and deploying data-driven solutions that support AI, ML, and BI initiatives. - Advanced SQL, Python knowledge and experience working with relational databases. - Experience with data science tools (e.g., IBM Watson, Azure AI Foundry, Dataiku, Databricks) and big data tools (e.g., Hadoop, Spark, Kafka).
- Proficiency in using programming languages for modelling, such as Python, Golang, Ruby, Perl, Scala, Java, SAS, R, MATLAB, C, and C++. - A strong interest in big data, machine learning, deep learning, and artificial intelligence. - An open-minded approach and strong problem-solving skills in data and AI domains.
<ul><li>Excellent business acumen and interpersonal skills, with the ability to work across business lines at a senior level to influence and effect change to achieve common data and AI goals.</li></ul></div>

Assistant Vice President, Data Science

Acquism SARL

<div>
 
 The AI Engineer is responsible for designing, developing, and deploying Agentic AI solutions and AI-enabled platforms within the Data &amp; AI Lab. The role focuses on building robust AI infrastructure, including integrations with cloud platforms, vector stores, document processing pipelines, and real-time data streaming capabilities.
 
 
 
 
 
 This position requires strong engineering skills to work with AI orchestration frameworks (MCP), cloud services (Azure, GCP), and modern AI tooling. The AI Engineer collaborates closely with AI Product Owners, Data Scientists, and other engineers to deliver production-grade AI solutions supporting banking operations and customer-facing applications.
 
 
 
 
 
 The role bridges AI research and production deployment, ensuring AI capabilities are scalable, maintainable, and aligned with enterprise architecture standards.
 
 
 
 
 
 
 
 Key Accountabilities
 
 
 
 
 
 1. Agentic AI Development
 
 
 <ul>
 <li>
 Design and implement Agentic AI solutions, including autonomous workflows, multi-agent systems, and AI orchestration patterns.
 </li>
 <li>
 Develop and maintain integrations with Model Context Protocol (MCP) and similar AI orchestration frameworks.
 </li>
 <li>
 Build and optimize AI pipelines that combine multiple AI capabilities into cohesive solutions.
 </li>
 </ul>
 
 
 2. AI Platform Engineering
 
 
 <ul>
 <li>
 Develop and maintain AI platform components, including vector stores, embedding pipelines, and retrieval systems.
 </li>
 <li>
 Implement document parsing, processing, and metadata extraction pipelines for knowledge management and RAG applications.
 </li>
 <li>
 Build and maintain APIs and integration layers for AI services consumption.
 </li>
 </ul>
 
 
 3. Cloud &amp; Infrastructure
 
 
 <ul>
 <li>
 Design and deploy AI solutions on cloud platforms (Azure, GCP) following best practices for scalability and security.
 </li>
 <li>
 Implement data streaming architectures using Kafka for real-time AI applications and event-driven AI workflows.
 </li>
 <li>
 Manage AI infrastructure, including model serving on OpenShift/Kubernetes, monitoring, and performance optimization.
 </li>
 </ul>
 
 
 4. Integration &amp; Delivery
 
 
 <ul>
 <li>
 Develop RESTful APIs and integration patterns for AI services consumption by internal and external applications.
 </li>
 <li>
 Collaborate with IT teams to integrate AI capabilities into existing systems and workflows.
 </li>
 <li>
 Participate in Agile delivery processes (Scrum, Kanban), contributing to sprint planning, code reviews, and continuous improvement.
 </li>
 </ul>
 
 
 5. Quality &amp; Standards
 
 
 <ul>
 <li>
 Ensure AI solutions comply with security, governance, and regulatory requirements.
 </li>
 <li>
 Maintain code quality through testing, documentation, CI/CD practices, and adherence to engineering best practices.
 </li>
 <li>
 Stay current with emerging AI technologies, tools, and frameworks to continuously improve delivery capabilities.
 </li>
 </ul>
 
 
 
 
 
 
 Key Competencies
 
 
 
 
 
 AI &amp; ML Engineering
 
 
 <ul>
 <li>
 Strong understanding of LLMs, embedding models, and generative AI architectures.
 </li>
 <li>
 Experience with AI orchestration frameworks, agent development, and multi-step AI workflows.
 </li>
 <li>
 Familiarity with vector databases (Azure AI Search, Pinecone, Weaviate, Milvus) and RAG patterns.
 </li>
 <li>
 Knowledge of deep learning frameworks (TensorFlow, PyTorch) and model deployment tools (MLflow, TFX).
 </li>
 </ul>
 
 
 Software Engineering
 
 
 <ul>
 <li>
 Advanced Python and relevant AI/ML libraries (LangChain, LlamaIndex, or similar).
 </li>
 <li>
 Experience with API development (REST) and microservices architecture.
 </li>
 <li>
 Advanced SQL skills (Stored Procedures, Window functions, Temp Tables, Recursive Queries).
 </li>
 <li>
 Git (GitHub/GitLab) for version control and code management.
 </li>
 </ul>
 
 
 Cloud &amp; Data Engineering
 
 
 <ul>
 <li>
 Experience with cloud platforms (Azure, GCP) and object storage (S3, GCS, ABS).
 </li>
 <li>
 Knowledge of data streaming technologies (Kafka) and workflow orchestration (Airflow, Apache NiFi).
 </li>
 <li>
 Containerization and orchestration using Docker and Kubernetes (OpenShift).
 </li>
 <li>
 Familiarity with Spark for data processing and MLOps practices.
 </li>
 </ul>
 
 
 Collaboration
 
 
 <ul>
 <li>
 Ability to work effectively in cross-functional teams with Data Scientists, ML Engineers, and Product Owners.
 </li>
 <li>
 Strong communication skills to explain technical concepts to non-technical stakeholders.
 </li>
 <li>
 Experience working in Agile/Scrum environments (Kanban, Scrum).
 </li>
 </ul>
 
 
 
 
 
 
 Qualifications &amp; Experience
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Software Engineering, Data Science, or a related field.
 </li>
 <li>
 4&ndash;6 years of experience in software engineering, with at least 2 years focused on AI/ML applications.
 </li>
 <li>
 Hands-on experience with cloud platforms (Azure or GCP) and containerization (Docker, OpenShift/K8s).
 </li>
 <li>
 Experience with document processing, metadata extraction, and knowledge management systems preferred.
 </li>
 <li>
 Banking or financial services industry experience is a plus.
 </li>
 <li>
 Relevant certifications (Azure AI Engineer, GCP ML Engineer) preferred.
 </li>
 </ul>
</div>

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