senior data scientist

TMC Middle East

<div>
 
 TMC Middle East is currently engaged in a project with one of our prestigious customers in UAE.
 
 
 We are seeking a highly capable Senior Data Scientist expert to help TMC to deliver this assignment.
 
 
 
 
 
 
 About the Role:
 
 
 
 Position: Senior Data Scientist
 
 
 Location: Abu Dhabi
 
 
 Experience: 8+ Years
 
 
 Preferred: Western &amp; Diverse candidates
 
 
 
 
 
 Technical Requirements
 
 
 ● Core AI/ML: Expert proficiency in Python and standard ML libraries (TensorFlow/PyTorch,
 
 
 Scikit-learn, Pandas, NumPy). Strong grasp of both supervised and unsupervised learning
 
 
 techniques.
 
 
 ● NLP &amp; LLMs: Deep experience with transformer-based models (GPT, BERT, Llama) and
 
 
 prompt engineering techniques (few-shot learning, fine-tuning) for domain-specific tasks.
 
 
 ● Data Engineering: Proficiency in handling complex data structures (JSON, XML) and
 
 
 familiarity with database querying (SQL/NoSQL) or graph data structures. Experience with
 
 
 data extraction from specialized formats is a significant plus.
 
 
 ● Backend Awareness: Understanding of how to expose models via RESTful APIs
 
 
 (Flask/FastAPI) and integrate them into larger software architectures.
 
 
 ● Statistics: Solid understanding of statistics, probability distribution, A/B testing. Adept at
 
 
 identifying and mitigating biases in datasets.
 
 
 
 
 
 Professional Qualifications
 
 
 ● Experience: 5+ years of experience in Data Science or Machine Learning, with a proven
 
 
 track record of deploying models into production environments.
 
 
 ● Domain Adaptability: Ability to quickly grasp complex domain terminology (e.g.,
 
 
 construction regulations, defense standards, supply chain taxonomies) and translate them
 
 
 into logic.
 
 
 ● Structured Delivery: Experience working in structured delivery models
 
 
 (Agile/Sprint-based) while adhering to rigorous validation and verification standards.
 
 
 ● Collaboration: Strong communication skills to work effectively with Domain Experts,
 
 
 Backend Engineers, and Product Managers to align model outputs with real-world
 
 
 business logic.
 
 
 
 
 
 Please share your CV with the title of
 
 Senior Data Scientist
 
 
 
 Mail ID : vigneshwari.sivalingam@tmc-employeneurs.com
 
</div>

Senior Data Scientist

Contango

<div>
 
 Contract duration:
 
 6 months (with a potential extension)
 
 
 
 Engagement Type:
 
 Full-time
 
 
 
 Start date:
 
 July 2026
 
 
 
 Location:
 
 Abu Dhabi (on-site)
 
 
 
 About Contango
 
 
 
 Contango is the strategic partner for transformative growth and sustained success for the ADQ portfolio. Our team excels in providing comprehensive growth solutions that combine global best practices with local market expertise. We focus on long-term value creation, empowering our clients to achieve the full scale of their aspirations. As a trusted advisor to ADQ's portfolio companies, Contango helps CEOs drive strategic growth initiatives, navigate disruptive forces, and maximize long-term value creation.
 
 
 
 Role Overview
 
 
 
 As a Senior Data Scientist, you will independently work on specific data projects and be responsible for implementing analytical solutions. You will design, build, deploy, and support end-to-end Data &amp; AI solutions. You will translate complex business challenges into scalable, production-ready analytics and machine learning systems, collaborating closely with product, data engineering, and architecture stakeholders to deliver measurable impact.
 
 
 
 Key Responsibilities
 
 
 
 
 Use Case Framing &amp; Solution Design
 
 
 
 <ul>
 <li>
 Translate client business problems into end-to-end system architectures that combine Data, ML, and software components.
 </li>
 <li>
 Lead the design of scalable, modular AI solutions, defining services, interfaces, and data flows.
 </li>
 <li>
 Make explicit trade-offs across performance, cost, latency, and maintainability.
 </li>
 <li>
 Define success metrics, SLAs, and non-functional requirements (reliability, security, scalability).
 
 
 </li>
 </ul>
 
 Data Engineering &amp; Feature Systems
 
 
 
 <ul>
 <li>
 Design and implement robust data pipelines (batch and streaming) with strong guarantees on quality, lineage, and observability.
 </li>
 <li>
 Build and manage feature pipelines and feature stores, ensuring consistency between training and inference.
 </li>
 <li>
 Collaborate with platform teams to define data models, schemas, and storage strategies.
 </li>
 <li>
 Enforce standards for data validation, testing, and monitoring within production systems.
 
 
 </li>
 </ul>
 
 Applied ML &amp; Production-Grade Development
 
 
 
 <ul>
 <li>
 Develop ML solutions using production-quality code (Python/JS), following software engineering best practices.
 </li>
 <li>
 Structure codebases into maintainable, testable modules, with clear separation of concerns.
 </li>
 <li>
 Implement unit, integration, and end-to-end tests for data and ML components.
 </li>
 <li>
 Package models and logic into deployable services (APIs, microservices, batch jobs) using modern frameworks.
 </li>
 <li>
 Balance model sophistication with system performance, latency, and operational constraints.
 
 
 </li>
 </ul>
 
 MLOps, DevOps &amp; Platform Integration
 
 
 
 <ul>
 <li>
 Build and maintain CI/CD pipelines for ML systems, including automated testing, validation, and deployment.
 </li>
 <li>
 Containerize and deploy services using Docker, Kubernetes, and cloud-native tooling.
 </li>
 <li>
 Implement model versioning, experiment tracking, and artifact management.
 </li>
 <li>
 Design monitoring and observability systems (logs, metrics, alerts) for both data and model performance.
 </li>
 <li>
 Automate retraining, rollback, and release strategies to ensure system resilience.
 
 
 </li>
 </ul>
 
 System Reliability, Scalability &amp; Security
 
 
 
 <ul>
 <li>
 Design systems for high availability, fault tolerance, and horizontal scalability.
 </li>
 <li>
 Optimize performance across data pipelines and inference services (latency, throughput, cost).
 </li>
 <li>
 Apply secure coding practices, access controls, and data protection standards.
 </li>
 <li>
 Manage technical debt and ensure long-term maintainability of production systems.
 
 
 </li>
 </ul>
 
 Documentation, Standards &amp; Engineering Excellence
 
 
 
 <ul>
 <li>
 Produce developer-focused documentation (APIs, architecture diagrams, runbooks).
 </li>
 <li>
 Establish and enforce coding standards, review processes, and engineering best practices.
 </li>
 <li>
 Build reusable libraries, SDKs, and internal frameworks to accelerate delivery.
 </li>
 <li>
 Drive continuous improvement in engineering maturity, tooling, and delivery practices across the consultancy.
 
 
 </li>
 </ul>
 
 Required Experience And Qualifications
 
 
 
 <ul>
 <li>
 Prior experience at management consulting firms and/or Big Tech is an advantage.
 </li>
 <li>
 Client-serving experience is an advantage.
 </li>
 <li>
 5+ years of experience in data science or a related analytical field.
 </li>
 <li>
 5+ years delivering end-to-end analytics/ML solutions from problem framing through deployment and ongoing monitoring in production.
 </li>
 <li>
 Experience applying software engineering methodologies and best practices, including coding standards, code reviews, build processes, testing, and security.
 </li>
 <li>
 Prior experience in developing AI solutions on public cloud services is an advantage.
 </li>
 <li>
 Bachelor&rsquo;s degree (Master&rsquo;s preferred) in a quantitative field (Computer Science, Data Science, Statistics, Mathematics, Engineering).
 </li>
 <li>
 Technical Expertise:
 </li>
 <ul>
 <li>
 Coding &amp; data querying: Python (pandas/NumPy) and SQL; writing clean, testable code with Git.
 </li>
 <li>
 Statistics &amp; experimentation: Probability/statistics, hypothesis testing, regression, and A/B testing/experimental design.
 </li>
 <li>
 ML modelling: Feature engineering, model selection, cross-validation, metrics, and hyperparameter tuning (supervised/unsupervised).
 </li>
 <li>
 Data prep &amp; analysis: ETL/EDA, data cleaning, handling missing/outliers, and building insight narratives with visuals.
 </li>
 </ul>
 </ul>
</div>

Senior Data Scientist (6 months contract)

<div>Contract duration: 6 months (with a potential extension) 
Engagement Type: Full-time 
Start date: July 2026 
Location: Abu Dhabi (on-site) 
About Contango:
Contango is the strategic partner for transformative growth and sustained success for the ADQ portfolio. Our team excels in providing comprehensive growth solutions that combine global best practices with local market expertise. We focus on long-term value creation, empowering our clients to achieve the full scale of their aspirations. As a trusted advisor to ADQ's portfolio companies, Contango helps CEOs drive strategic growth initiatives, navigate disruptive forces, and maximize long-term value creation.
Role Overview
As a Senior Data Scientist, you will independently work on specific data projects and be responsible for implementing analytical solutions. You will design, build, deploy, and support end-to-end Data &amp; AI solutions. You will translate complex business challenges into scalable, production-ready analytics and machine learning systems, collaborating closely with product, data engineering, and architecture stakeholders to deliver measurable impact. 
Key Responsibilities
Use Case Framing &amp; Solution Design<ul><li>
Translate client business problems into end-to-end system architectures that combine Data, ML, and software components.</li><li>
Lead the design of scalable, modular AI solutions, defining services, interfaces, and data flows.</li><li>
Make explicit trade-offs across performance, cost, latency, and maintainability.</li><li>
Define success metrics, SLAs, and non-functional requirements (reliability, security, scalability).</li></ul>
Data Engineering &amp; Feature Systems<ul><li>
Design and implement robust data pipelines (batch and streaming) with strong guarantees on quality, lineage, and observability.</li><li>
Build and manage feature pipelines and feature stores, ensuring consistency between training and inference.</li><li>
Collaborate with platform teams to define data models, schemas, and storage strategies.</li><li>
Enforce standards for data validation, testing, and monitoring within production systems.</li></ul>
Applied ML &amp; Production-Grade Development<ul><li>
Develop ML solutions using production-quality code (Python/JS), following software engineering best practices.</li><li>
Structure codebases into maintainable, testable modules, with clear separation of concerns.</li><li>
Implement unit, integration, and end-to-end tests for data and ML components.</li><li>
Package models and logic into deployable services (APIs, microservices, batch jobs) using modern frameworks.</li><li>
Balance model sophistication with system performance, latency, and operational constraints.</li></ul>
MLOps, DevOps &amp; Platform Integration<ul><li>
Build and maintain CI/CD pipelines for ML systems, including automated testing, validation, and deployment.</li><li>
Containerize and deploy services using Docker, Kubernetes, and cloud-native tooling.</li><li>
Implement model versioning, experiment tracking, and artifact management.</li><li>
Design monitoring and observability systems (logs, metrics, alerts) for both data and model performance.</li><li>
Automate retraining, rollback, and release strategies to ensure system resilience.</li></ul>
System Reliability, Scalability &amp; Security<ul><li>
Design systems for high availability, fault tolerance, and horizontal scalability.</li><li>
Optimize performance across data pipelines and inference services (latency, throughput, cost).</li><li>
Apply secure coding practices, access controls, and data protection standards.</li><li>
Manage technical debt and ensure long-term maintainability of production systems.</li></ul>
Documentation, Standards &amp; Engineering Excellence<ul><li>
Produce developer-focused documentation (APIs, architecture diagrams, runbooks).</li><li>
Establish and enforce coding standards, review processes, and engineering best practices.</li><li>
Build reusable libraries, SDKs, and internal frameworks to accelerate delivery.</li><li>
Drive continuous improvement in engineering maturity, tooling, and delivery practices across the consultancy.</li></ul>
Required Experience and Qualifications<ul><li>
Prior experience at management consulting firms and/or Big Tech is an advantage.</li><li>
Client-serving experience is an advantage.</li><li>
5+ years of experience in data science or a related analytical field.</li><li>
5+ years delivering end-to-end analytics/ML solutions from problem framing through deployment and ongoing monitoring in production.</li><li>
Experience applying software engineering methodologies and best practices, including coding standards, code reviews, build processes, testing, and security.</li><li>
Prior experience in developing AI solutions on public cloud services is an advantage.</li><li>
Bachelor&rsquo;s degree (Master&rsquo;s preferred) in a quantitative field (Computer Science, Data Science, Statistics, Mathematics, Engineering).</li><li>
Technical Expertise:<ul><li>
Coding &amp; data querying: Python (pandas/NumPy) and SQL; writing clean, testable code with Git.</li><li>
Statistics &amp; experimentation: Probability/statistics, hypothesis testing, regression, and A/B testing/experimental design.</li><li>
ML modelling: Feature engineering, model selection, cross-validation, metrics, and hyperparameter tuning (supervised/unsupervised).</li><li>
Data prep &amp; analysis: ETL/EDA, data cleaning, handling missing/outliers, and building insight narratives with visuals.</li></ul></li></ul></div>

Arab Monetary Fund

<div>
 
 
 Job Purpose:
 
 
 
 The Data Scientist supports the design and implementation of advanced data analytics solutions to enhance AMF&rsquo;s research, policy analysis, and decision-making capabilities. The role focuses on applying machine learning, AI, and statistical modelling to unlock insights, strengthen data integration, and advance innovation within the organization.
 
 
 
 
 
 Roles and Responsibilities
 
 
 Key Accountabilities
 
 <ul>
 <li>
 Design and implement statistical models, machine learning algorithms, and AI tools for large-scale structured and unstructured data
 </li>
 <li>
 Conduct predictive and prescriptive analytics to support economic and financial policy initiatives
 </li>
 <li>
 Continuously enhance models for performance, accuracy, and scalability
 </li>
 <li>
 Clean, transform, and manage diverse data sources using automated ETL pipelines
 </li>
 <li>
 Collaborate with IT to optimize data infrastructure for secure and efficient access
 </li>
 <li>
 Ensure data quality and alignment with organizational data architecture standards
 </li>
 <li>
 Partner with research teams on data-driven approaches for frontier topics (e.g., digital economy, AI, climate, financial innovation)
 </li>
 <li>
 Apply advanced techniques such as deep learning and NLP to enhance research
 </li>
 <li>
 Monitor developments in data science and introduce relevant tools and techniques
 </li>
 <li>
 Develop dashboards and visualizations to communicate insights to policymakers
 </li>
 <li>
 Create data storytelling outputs for publications, policy discussions, and training
 </li>
 <li>
 Ensure consistent and accessible data presentation across platforms and reports
 </li>
 <li>
 Comply with data protection regulations, internal policies, and ethical AI standards
 </li>
 <li>
 Support implementation of data governance frameworks and quality standards
 </li>
 <li>
 Participate in audits and compliance reviews related to data use and access
 </li>
 </ul>
 
 
 
 
 
 Job Qualifications and Requirements:
 
 
 
 
 
 
 
 Knowledge and Experience:
 
 
 <ul>
 <li>
 Minimum 5 years of experience in:
 </li>
 <li>
 Data science
 </li>
 <li>
 Machine learning
 </li>
 <li>
 Advanced analytics
 </li>
 <li>
 Preferably within economic, financial, or public policy sectors
 </li>
 <li>
 Proven ability to develop predictive models and deliver actionable insights
 </li>
 </ul>
 
 
 
 
 
 Education:
 
 
 <ul>
 <li>
 Bachelor&rsquo;s degree in:
 </li>
 <li>
 Data science
 </li>
 <li>
 Computer science
 </li>
 <li>
 Statistics
 </li>
 <li>
 Economics Or a related field
 </li>
 </ul>
 
 
 
 
 
 Certifications (Preferred):
 
 
 <ul>
 <li>
 Recognized certifications such as:
 </li>
 <li>
 Google Data Analytics
 </li>
 <li>
 IBM Data Science
 </li>
 <li>
 SAS
 </li>
 <li>
 CAP (Certified Analytics Professional)
 </li>
 </ul>
 
 
 
</div>

Data Scientist

Klanik

<div>
 
 We are currently looking for a talented
 
 Data Scientist &ndash;Team Lead &amp; Data Scientist
 
 to
 
 
 our team in
 
 Abu Dhabi
 
 !!!
 
 
 
 
 
 
 Requirements:
 
 
 
 
 
 <ul>
 <li>
 Computer vision (object detection, tracking, re-ID, action recognition, segmentation);
 </li>
 <li>
 YOLO, ViT / Transformer models, RF-DETR;
 </li>
 <li>
 VLMs &amp; fine-tuning; &bull; optimisation (TensorRT, ONNX, OpenVINO);
 </li>
 <li>
 Python, PyTorch / TensorFlow;
 </li>
 <li>
 Full MLOps lifecycle; edge-AI constraints (latency, FPS, GPU);
 </li>
 <li>
 Multi-camera tracking;
 </li>
 <li>
 Team leadership, code review &amp; mentoring;
 </li>
 <li>
 Stakeholder communication &amp; solution architecture.
 </li>
 <li>
 CV expertise (detection, tracking, segmentation)
 </li>
 <li>
 Hands-on with YOLO, RF-DETR and similar models
 </li>
 <li>
 Experience in fine-tuning Vision-Language Models (VLMs)
 </li>
 <li>
 Quantization Experience
 </li>
 <li>
 Data preprocessing, augmentation &amp; dataset curation
 </li>
 <li>
 Model training, tuning, evaluation (mAP, precision/recall)
 </li>
 <li>
 Experience with video datasets &amp; annotation feedback loop
 </li>
 <li>
 Experiment tracking (MLflow/W&amp;B)
 </li>
 <li>
 Model optimization basics (TensorRT, ONNX)
 </li>
 <li>
 Understanding of real-world deployment challenges
 </li>
 </ul>
 
 
 
 
 
 Educational
 
 : Computer science or engineering (preferred)
 
 
 
 
 <ul>
 <li>
 3-7 years of hands-on experience in AI/ML applications
 </li>
 <li>
 8-15 years leading and coaching teams, demonstrable track record of projects delivered, bringing value to its recipients
 </li>
 </ul>
 
 
 
 
 You are looking for a great opportunity to be part of an amazing challenge. This job is for you!!!
 
 
 
 
 
 
 
 About KLANIK:
 
 
 
 
 
 
 
 Klanik is an IT consulting company providing solutions to some of the world&rsquo;s largest industrial and services groups for 10 years.
 
 
 We support our clients with the creation and development of their new products and services around 4 main business lines:
 
 
 ➜ Information Technology and Systems (IT &amp; IS)
 
 
 ➜ DevOps and Cloud
 
 
 ➜ Big Data and AI
 
 
 ➜ Cybersecurity
 
 
 
 
 
 Klanik is a community of +500 passionate experts empowering clients in 6 countries throughout Europe, Middle East and the Americas.
 
 
 Our Team is dedicated to offer our global clients project support while guaranteeing a consistent level of service alongside a commitment to excellence and strong core values.
 
 
 We&rsquo;re always looking for smart, talented, driven, down-to-earth, fun-to-work-with people who want to make a positive and meaningful impact!
 
 
 
 
 
 
 
 Being a &ldquo;KLANIK&rdquo;
 
 
 
 
 That means being an out-of-the-box thinker, a precursor who is keen to create, innovate and collaborate in an environment where you can enjoy being yourself and valued for your ideas. It&rsquo;s being part of a community of experts who have a passion for and stay abreast of the latest technologies.
 
 
 Do you feel like a technology pioneer, full of creative ideas, supported by strong know-how and expertise in the latest technologies? We believe you could be a good fit for our culture!
 
 
 
 
 
 
 
 Our culture is our pride
 
 
 
 
 At Klanik, we believe that we are stronger together than we are alone. Trust, ethics, respect, and transparency are deeply fixed in our culture, alongside the pride in a job well done.
 
 
 Our community motto is that people give the best of themselves when they feel listened to, respected, and empowered to develop themselves. Therefore, the better we treat our people, the bigger the success they will achieve. We apply those values on a day-to-day basis among our teams, with our clients, and even with our competitors.
 
 
 Being part of the Klanik community means relying on each other, learning from each other, and growing all together. Our community fosters creativity, innovation, and development within our teams to tackle more and more complex challenges and deliver the best solutions to our clients.
 
</div>

Data Scientist – Team Lead

Yokogawa

<div>Not just a job, but a career
Yokogawa, award winner for &lsquo;Best Asset Monitoring Technology&rsquo; and &lsquo;Best Digital Twin Technology&rsquo; at the HP Awards, is a leading provider of industrial automation, test and measurement, information systems and industrial services in several industries.
Our aim is to shape a better future for our planet through supporting the energy transition, (bio)technology, artificial intelligence, industrial cybersecurity, etc. We are committed to the United Nations sustainable development goals by utilizing our ability to measure and connect.
About the Team 
Our 18,000 employees work in over 60 countries with one corporate mission, to &quot;co-innovate tomorrow&quot;. We are looking for dynamic colleagues who share our passion for technology and care for our planet. In return, we offer you great career opportunities to grow yourself in a truly global culture where respect, value creation, collaboration, integrity, and gratitude are highly valued and exhibited in everything we do.
Job Purpose
The Senior AI Process Consultant is responsible for architecting AI/ML-based process solutions and leading AI-driven operational transformation across Oil &amp; Gas, Petrochemical, and other process-industry domains. Bridging deep process-operations credibility with modern AI, architecture governance, and commercial value thinking, the role owns AI/ML business outcomes, solution architecture, and implementation strategy; translating operational problem statements into scalable, auditable solutions and guiding pilot-to-scale adoption. The role collaborates with and supports the Digital Enterprise Business Unit / Solution Division to build differentiated capabilities and accelerate growth across the YMA region.
Key Responsibilities &amp; Accountabilities
1. Technical Analysis &amp; Solution Architecture<ul><li>
Perform high level technical analysis leveraging AI/ML and Digital Twin technology, including data gathering and gap analysis, to identify opportunities for resolving customers' process problems.</li><li>
Define solution architectures and develop detailed design specifications for complex AI solutions.</li><li>
Define functional requirements through client interviews, documentation analysis, and workflow / process mapping.</li><li>
Provide expert technical judgment to estimate the effort required to implement complex AI solutions.</li></ul>
2. Business Case &amp; Portfolio Development<ul><li>
Identify and develop business cases for focused AI applications and support their implementation and successful proliferation within the assigned process industry / application.</li><li>
Advise customers on suitable AI/ML solutions and provide overall business portfolio management, solution design, and demonstration functions.</li><li>
Own and drive the preparation of Feasibility Study/ Business Case reports to support the customer for FID/Budgeting approvals.</li></ul>
3. Client Engagement &amp; Relationship Management<ul><li>
Engage proactively with customers to understand expectations and ensure they are consistently met through AI/ML solutions.</li><li>
Define the overall technical approach for AI implementation, integrating simulation / Digital Twin and, where applicable, APC/RTO technologies.</li><li>
Develop solutions that resolve customer process problems and support business growth in the assigned technical area(s).</li><li>
Assist existing customers in problem resolution, identify future opportunities, and provide ongoing service support to clients under contract.</li></ul>
4. Project Delivery &amp; Quality Assurance<ul><li>
Own technical deliverables across the entire lifecycle of solution-development projects.</li><li>
Ensure the timeliness and quality of assigned deliverables while supporting the broader delivery team.</li><li>
Ensure AI best-practice approaches, standards, and methods are applied appropriately throughout delivery.</li><li>
Participate actively as a member of technical project teams, building and maintaining effective working relationships.</li></ul>
5. Knowledge Sharing, Training &amp; Enablement<ul><li>
Support Smart Manufacturing / Digital Transformation (SM/DX) training efforts by providing input, documenting requirements, and supporting the design and delivery of AI-related training programs.</li></ul>
6. Sales Collaboration &amp; Growth<ul><li>
Partner effectively with sales personnel to position solutions, support opportunity development, and secure orders.</li></ul>
Qualification and Experience:<ul><li>
Bachelors or Masters in Chemical Engineering. preferred, with a minimum of 15 years of industrial process experience spanning APC/RTO-related software development, engineering, and business development.</li><li>
Minimum 4 years' experience leading, architecting, or implementing AI/ML applications and AI-driven transformation initiatives in an industrial setting; knowledge of AI for process control is an advantage.</li><li>
Strong foundation in AI/ML methods, the model lifecycle, and best-practice deployment and governance approaches.</li><li>
Strong problem-solving skills with an emphasis on solution development.</li><li>
Work location: Yokogawa Global AI CoE Centre, United Arab Emirates (UAE).</li><li>
Willing and able to travel throughout the YMA region and globally as required.</li></ul>
Competencies &amp; Skills
<ul><li>Strong foundation in AI/ML solution design, methodology, and solution / enterprise architecture, including architecture governance (e.g. TOGAF).</li><li>
Strong exposure to Machine Learning technologies, including automated data collection, historian applications (OSI PI a plus), data visualization, manufacturing quality and efficiency, SCADA, automated decision and control, workflow, database applications, and scheduling.</li><li>
Awareness of, or working knowledge of, Python, R, and common Machine Learning frameworks is an added value.</li><li>
Strong knowledge of IIoT integration concepts and architectures.</li><li>
Up-to-date awareness of the latest AI and Digital Twin market trends.</li><li>
Knowledge of the Oil &amp; Gas, Petrochemical, or specialty chemicals domains is a plus.</li><li>
Experience with advanced process applications (e.g. MPA / APC / RTO) is an advantage.</li><li>
Strong communicator and collaborative team player with the ability to energize and influence others, including senior and executive stakeholders.</li><li>
Strong problem-solving orientation with a focus on practical solution development.</li></ul>
&ldquo;All appointments are subject to the successful completion of applicable background verification checks.&rdquo;
Yokogawa is an Equal Opportunity Employer. Yokogawa wants a diverse, equitable and inclusive culture. We will actively recruit, develop, and promote people from a variety of backgrounds who differ in terms of experience, knowledge, thinking styles, perspective, cultural background, and socioeconomic status. We will not discriminate based on race, skin color, age, sex, gender identity and expression, sexual orientation, religion, belief, political opinion, nationality, ethnicity, place of origin, disability, family relations or any other circumstances. Yokogawa values differences and enables everyone to belong, contribute, succeed, and demonstrate their full potential.
Are you being referred to one of our roles? If so, ask your connection at Yokogawa 
about our Employee Referral process!</div>

Senior AI/ML Process Consultant/Engineer

ENEC Operations

<div>
 
 The Data and AI Engineering Expert is a senior hands-on technical authority responsible for the design, development, and operationalization of enterprise data and AI platforms across ENEC. This role leads the engineering delivery of scalable, secure, and compliant data pipelines, AI/ML solutions, and analytics platforms leveraging technologies such as Databricks (Delta Lake, Unity Catalog, MLflow, Delta Live Tables), Microsoft Azure (Azure Data Factory, Azure ML, Microsoft Fabric, Azure AI Foundry, Copilot Studio), Collibra (Data Governance &amp; Data Quality), Power BI, and OT/industrial data systems including PI System, SCADA, and DCS.
 
 
 
 
 
 The Expert applies deep engineering expertise to architect and deliver production-grade solutions, ensures data governance and quality standards are upheld, and drives the adoption of AI and analytics capabilities that directly support ENEC's mission of safe, innovative, and efficient nuclear energy generation. This role acts as a principal technical contributor and mentor, enabling operational excellence and digital transformation at scale.
 
 
 Activity: Data Engineering Architecture &amp; Platform Development
 
 
 
 
 
 Responsibilities and Accountabilities:
 
 
 &bull; Design, build, and maintain scalable, secure, and high-performance data platforms including Lakehouse architectures using Databricks Delta Lake, Unity Catalog, and Delta Live Tables (DLT).
 
 
 &bull; Develop and operationalize robust data pipelines, ETL/ELT workflows, and integration frameworks using Azure Data Factory, Databricks Workflows, and related orchestration tools.
 
 
 &bull; Architect and implement data models, semantic layers, and data products that serve enterprise analytics, AI, and reporting needs.
 
 
 &bull; Ensure seamless integration of OT and industrial data sources &mdash; including PI System, SCADA, and DCS &mdash; into enterprise data platforms.
 
 
 &bull; Design and implement APIs, data contracts, and integration frameworks to enable enterprise-wide data consumption.
 
 
 &bull; Ensure platform scalability, resilience, high availability, and disaster recovery capabilities in compliance with nuclear and regulatory requirements.
 
 
 &bull; Apply and enforce data standards, naming conventions, and metadata management practices across all data assets using Collibra and Unity Catalog.
 
 
 Responsibilities and Accountabilities:
 
 
 &bull; Design, develop, and deploy production-grade machine learning, deep learning, and generative AI models using Azure ML, MLflow, and Databricks ML.
 
 
 &bull; Engineer end-to-end MLOps pipelines covering model training, versioning, validation, deployment, monitoring, and retraining &mdash; ensuring reliability and reproducibility.
 
 
 &bull; Build and operationalize AI-powered solutions including predictive analytics, anomaly detection, natural language processing (NLP), and GenAI/LLM-based applications using Azure AI Foundry and Copilot Studio.
 
 
 &bull; Develop BI and semantic models in Microsoft Fabric and Power BI, enabling self-service analytics and executive reporting across the organization.
 
 
 &bull; Apply Responsible AI principles and governance frameworks to all AI and ML solutions, ensuring explainability, fairness, and compliance.
 
 
 &bull; Conduct rigorous model evaluation, performance benchmarking, and validation to ensure solutions meet accuracy, reliability, and safety standards.
 
 
 &bull; Contribute to the continuous improvement of AI engineering standards, patterns, and reusable components across the enterprise.
 
 
 Activity: Advanced Subject Matter Knowledge
 
 
 
 
 
 Responsibilities and Accountabilities:
 
 
 &bull; Demonstrates deep, hands-on expertise in enterprise data engineering, AI/ML platform development, and cloud-native architectures within highly regulated environments.
 
 
 &bull; Maintains current knowledge of global trends in data engineering, MLOps, GenAI, cloud technologies (Azure, hybrid, on-premise), and nuclear industry compliance requirements.
 
 
 &bull; Understands the full lifecycle of data and AI platforms including design, development, testing, deployment, monitoring, and decommissioning.
 
 
 &bull; Applies comprehensive knowledge of data governance frameworks (Collibra, Unity Catalog), master data management, data quality, and lineage management.
 
 
 &bull; Possesses strong awareness of OT/IT convergence, industrial data systems (PI System, SCADA, DCS), and their integration into enterprise analytics environments.
 
 
 &bull; Maintains expert-level proficiency in programming languages and frameworks including Python, SQL, Spark, and relevant AI/ML libraries.
 
 
 Activity: Complex Problem Solving &amp; Innovation
 
 
 
 
 
 Responsibilities and Accountabilities:
 
 
 &bull; Tackles complex, ambiguous engineering challenges in data platform architecture, AI model development, and system integration &mdash; delivering innovative, compliant, and production-ready solutions.
 
 
 &bull; Leads root cause analysis and corrective/preventive actions for platform incidents, data quality failures, model degradation, and system disruptions.
 
 
 &bull; Develops and implements robust engineering solutions to address performance bottlenecks, scalability constraints, and integration challenges.
 
 
 &bull; Navigates conflicting technical requirements, evolving regulatory standards, and stakeholder needs to deliver optimal engineering outcomes.
 
 
 &bull; Drives continuous improvement by benchmarking against global engineering best practices, evaluating emerging technologies, and implementing lessons learned.
 
 
 &bull; Leads proof-of-concept and pilot initiatives to evaluate new data and AI technologies and engineering approaches
 
 
 
 
 
 Activity: Applied Skills &amp; Professional Know-How
 
 
 
 
 
 Responsibilities and Accountabilities:
 
 
 &bull; Expertly engineers, deploys, and operates scalable, secure, and compliant data and AI platforms across Databricks, Azure, and OT environments.
 
 
 &bull; Applies hands-on proficiency in building data pipelines, ML models, GenAI solutions, BI semantic models, and governance frameworks in production environments.
 
 
 &bull; Mentors and develops junior and mid-level data engineers, data scientists, and analytics engineers &mdash; building future-ready technical capabilities.
 
 
 &bull; Leads technical design reviews, code reviews, and architecture assessments to uphold engineering quality and best practices.
 
 
 &bull; Effectively translates complex technical concepts into clear, actionable insights for both technical and non-technical stakeholders.
 
 
 &bull; Documents solutions, engineering patterns, and technical standards to build institutional knowledge and enable knowledge transfer.
 
 
 Activity: Security, Governance &amp; Compliance
 
 
 
 
 
 Responsibilities and Accountabilities:
 
 
 &bull; Design and enforce data security, access control, and privacy frameworks across all data and AI platforms using Unity Catalog, Collibra, and Azure security controls.
 
 
 &bull; Ensure all data engineering and AI solutions comply with national and international nuclear regulations, FANR requirements, and Responsible AI principles.
 
 
 &bull; Maintain comprehensive data lineage, audit trails, and documentation to support regulatory, audit, and governance requirements.
 
 
 &bull; Implement and enforce data quality frameworks and data governance policies using Collibra Data Quality and Unity Catalog.
 
 
 &bull; Conduct regular platform security assessments, data quality audits, and compliance reviews.
 
 
 &bull; Oversee risk management, audit readiness, and the implementation of robust technical controls for all platform operations.
 
 
 
 
 
 
 
 
 
 Minimum
 
 
 
 &bull; Bachelor&rsquo;s degree in computer science, Information Technology, Data Management, Engineering, or related discipline.
 
 
 &bull; Minimum 12+ years' hands-on experience in enterprise data engineering, AI/ML platform development, and cloud-native architectures, including production deployment and operations.
 
 
 &bull; Experience embedding governance within modern data platforms (preferably Databricks) and integrating SAP/Oracle ERP domains.
 
 
 
 
 
 
 
 
 
 Preferred
 
 
 
 
 
 
 &bull; Master&rsquo;s degree in a relevant field.
 
 
 &bull; Experience in energy, utilities, nuclear, financial services, government, or other highly regulated sectors.
 
 
 &bull; Demonstrated AI governance experience (provenance, bias monitoring, model data requirements) in data bricks or similar AI/analytics platforms exposure.
 
 
 &bull; Demonstrated experience leading large-scale data and AI engineering programs
 
 
 
 
 
 
 
 
 
 Certification -
 
 
 
 
 
 
 Multiple cloud and AI certifications. Microsoft Fabric, Azure AI Foundry, or Databricks Unity Catalog specialty certifications. Industry publications or contributions in data engineering or AI.
 
 
 
 
 
 Relevant certifications in one or more of: Databricks (Data Engineer Associate/Professional, ML Professional), Microsoft Azure (Data Engineer, AI Engineer, Solutions Architect), MLOps, or Data Governance (Collibra).
 
</div>

Data and AI Engineering Expert

Explore 215+ Senior data scientist in Abu Dhabi this July. Find roles across top companies and apply now to boost your career.

215 senior data scientist jobs In Abu Dhabi

Senior Data Scientist

Senior Data Scientist (6 months contract)

Senior Data Scientist (6 months contract)

Data Scientist

Data Scientist – Team Lead

Senior AI/ML Process Consultant/Engineer

Data and AI Engineering Expert