Senior Data Scientist - AI and Machine Learning

Talabat; Dubai; On-site; AED 335,000 - 502,000; Full time

Job summary

talabat is seeking a Senior Data Scientist in Dubai, UAE, to own end-to-end AI/ML initiatives from problem discovery to deployment and monitoring. The role collaborates with product, data science, ML engineering, and engineering teams to deliver scalable AI solutions with measurable business impact.

The ideal candidate has 5+ years in data science, ML engineering, or related fields, with strong Python/SQL, ML frameworks, and MLOps experience.

Qualifications

• At least 5 years of professional experience spanning data science, ML engineering, and related AI disciplines.
• Bachelor’s degree required; postgraduate degree is a plus.
• Strong hands-on expertise in ML frameworks, deploying models to production.

Responsibilities

• Translate ambiguous business challenges into clearly defined ML problems with measurable success criteria.
• Identify high-impact opportunities where ML or generative AI can improve outcomes.
• Design and build end-to-end ML systems for production environments.
• Develop data pipelines supporting feature creation, model training, evaluation, deployment, and monitoring.
• Perform feature engineering on large-scale datasets.
• Train, evaluate, and improve ML models using appropriate algorithms.
• Build production-grade code for ML applications and supporting services.
• Deploy models into production and maintain reliable serving workflows.
• Monitor model performance, data quality, and business impact after deployment.
• Apply generative AI and LLMs to data enrichment, automation, and decision support.
• Develop automated analytical reporting for stakeholders.
• Design A/B tests and multivariate experiments to measure impact.

Skills

Machine learning

Generative AI

NLP

Deep learning

Python

SQL

Model deployment

Airflow

MLOps

Experiment design

Statistical analysis

Production ML

Education

Bachelor's degree in engineering, computer science, technology, or related field

Postgraduate qualification (advantageous)

Tools

Scikit-learn

XGBoost

LightGBM

CatBoost

Keras

TensorFlow

PyTorch

Transformers

Job description

Job Snapshot

Senior Data Scientist - AI and Machine LearningLocation: Dubai, United Arab EmiratesIndustry: InternetFunction: R and D-ScienceExperience: Minimum 5 years across data science, machine learning engineering, and generative AIJob Type: Full-time

Senior Data Scientist - AI and Machine Learning in Dubai, United Arab Emirates is an Internet opportunity for an experienced data science professional responsible for designing, building, deploying, and improving production-grade machine learning and generative AI systems. This hiring role is suited to candidates seeking a senior AI and machine learning job in Dubai with end-to-end ownership across problem formulation, feature engineering, model development, experimentation, MLOps, LLM applications, production monitoring, and data-driven product decision-making.

Job Details

Country: United Arab EmiratesCity: DubaiIndustry: InternetFunction: R and D-ScienceSalary: 30000-45000 Estimated salary range based on similar jobs in the job city; please confirm the final offer with the employer. Gender: AnyCandidate Nationality: AnyJob Type: Full-time

Role Context

The Senior Data Scientist works within talabat 's global AI hub to develop intelligent systems that improve decisions across consumer products and business operations. The role owns a defined domain from initial business problem discovery through data modeling, model training, deployment, serving, experimentation, and ongoing performance monitoring. This position combines advanced data science with significant engineering responsibility. The successful candidate will work closely with Product Managers, Business Managers, Data Scientists, Machine Learning Engineers, and Engineering teams to convert complex operational and customer problems into scalable AI solutions that deliver measurable business impact.

Key Responsibilities

• Translate ambiguous business challenges into clearly defined machine learning and data science problems with measurable success criteria.
• Identify high-impact opportunities where machine learning, generative AI, or advanced analytics can improve product and business outcomes.
• Design and build end-to-end machine learning systems suitable for production environments.
• Develop reliable data pipelines supporting feature creation, model training, evaluation, deployment, and monitoring.
• Perform feature engineering using large-scale behavioral, transactional, and product datasets.
• Train, evaluate, compare, and improve machine learning models using appropriate algorithms and architectures.
• Select practical modeling approaches based on business value, scalability, interpretability, performance, and operational complexity.
• Build production-grade code for machine learning applications and supporting services.
• Deploy trained models into production and maintain dependable model-serving workflows.
• Monitor model performance, data quality, drift, reliability, and downstream business impact after deployment.
• Apply generative AI and large language models to data enrichment, smart content understanding, automation, and decision-support use cases.
• Evaluate LLM approaches and determine when fine-tuning, prompting, retrieval, or traditional machine learning provides the most appropriate solution.
• Build and maintain data models and reusable features supporting model development.
• Develop automated analytical reporting that provides actionable insights for business and product stakeholders.
• Conduct systematic data profiling to understand source data, quality issues, generating systems, and behavioral patterns.
• Work closely with Engineering teams to understand instrumentation, event generation, and data dependencies.
• Design A-B tests and multivariate experiments to measure product and model impact.
• Analyze experiment results using appropriate statistical methods and translate findings into clear recommendations.
• Apply inferential, causal, predictive, and descriptive techniques according to the problem being solved.
• Partner with Product teams to understand user behavior, conversion, engagement, retention, and other product health indicators.
• Collaborate with business stakeholders to convert analytical insights into practical actions and strategic decisions.
• Develop scalable recommendation, NLP, deep learning, pattern recognition, or predictive modeling solutions where appropriate.
• Use Python and SQL extensively for reproducible analysis, modeling, validation, and production workflows.
• Build and orchestrate training and data workflows using platforms such as Airflow.
• Contribute to stronger MLOps practices covering repeatability, deployment, observability, testing, monitoring, and model lifecycle management.
• Improve internal machine learning tools, development practices, and engineering standards.
• Mentor other Data Scientists and support their technical development.
• Share machine learning knowledge through internal training, documentation, technical discussions, and practical coaching.
• Challenge unnecessary complexity and favor simpler solutions when they can deliver equal or stronger business outcomes.
• Maintain full ownership of assigned machine learning initiatives from discovery through measurable production impact.

Ideal Profile

• The preferred candidate has at least 5 years of professional experience spanning data science, machine learning engineering, generative AI, or closely related technical disciplines, with demonstrated experience deploying machine learning models into production.
• A bachelor 's degree in engineering, computer science, technology, or a related field is required.
• A postgraduate qualification would be advantageous but is not essential.
• Deep technical expertise should include machine learning, deep learning, generative AI, NLP, recommendation systems, pattern recognition, and data mining.
• Candidates should have strong hands-on experience with relevant frameworks such as Scikit-learn, XGBoost, LightGBM, CatBoost, Keras, TensorFlow, PyTorch, Transformers, and LLM development techniques.
• Strong software engineering fundamentals are important.
• The successful candidate should be comfortable with data structures, algorithms, system design, ML system architecture, clean production code, and designing reliable services rather than limiting work to notebook-based analysis.
• Advanced Python and SQL capabilities are required, together with practical experience building data and machine learning pipelines.
• Exposure to Airflow, BigQuery, Google Cloud Platform, dimensional modeling, and feature engineering will be valuable.
• The position also requires strong statistical foundations covering experimentation, inferential methods, causal analysis, predictive modeling, and rigorous interpretation of results.
• Candidates should demonstrate ownership, curiosity, practical problem solving, strong collaboration, and clear communication.
• Experience developing machine learning systems for online consumer products would be especially relevant.

Skills Set

• Machine learning
• Generative AI
• Artificial intelligence
• Large language models
• LLM fine-tuning
• Natural language processing
• Deep learning
• Recommendation systems
• Pattern recognition
• Data mining
• Python
• SQL
• Scikit-learn
• XGBoost
• LightGBM
• CatBoost
• Support Vector Machines
• Keras
• TensorFlow
• PyTorch
• Transformers
• Feature engineering
• Data pipelines
• Training pipelines
• Airflow
• MLOps
• Model deployment
• Model serving
• Model monitoring
• Model evaluation
• ML system design
• Software engineering
• Data structures
• Algorithms
• System design
• Production machine learning
• A-B testing
• Multivariate testing
• Experiment design
• Statistical analysis
• Causal analysis
• Predictive modeling
• Inferential statistics
• Data modeling
• Dimensional modeling
• Product analytics
• Conversion analysis
• Engagement analysis
• Retention analysis
• BigQuery
• Google Cloud Platform
• Data profiling
• Automated reporting
• Technical mentoring

Why Join Us

This opportunity places the successful candidate within a high-impact AI environment where machine learning systems can influence millions of customer interactions and large-scale business decisions across talabat 's regional platform. The role provides substantial ownership over the complete ML lifecycle rather than separating modeling, engineering, deployment, and measurement into isolated responsibilities. Working within the global AI hub also offers exposure to complex consumer data, generative AI applications, recommendation systems, experimentation, production MLOps, and scalable decision systems. For an experienced Data Scientist, the position provides room to deepen technical leadership while mentoring colleagues and shaping how advanced AI is applied across one of the region 's largest digital commerce ecosystems.

About the Company

talabat is a leading on-demand food, grocery, and Q-commerce technology platform operating across the Middle East. The company uses data, artificial intelligence, logistics technology, and digital commerce systems to connect customers with restaurants and local retailers while supporting riders and partners across multiple regional markets.