66 Feature Engineering jobs in Dubai

The Director of Data Analysis is responsible for collecting processing and analysing real estate data from various sources with the aim of providing accurate data-driven insights that support strategic decision-making in the real estate sector in United Arab Emirates. The role focuses on enhancing market transparency developing sector-wide performance indicators and supporting policy formulation and investment planning based on data.

Responsibilities

• Collecting and analyzing real estate data from multiple sources
• Processing and cleaning data to ensure its accuracy consistency and readiness for analysis
• Analyzing real estate data to extract and update the real estate index which enhances market transparency
• Preparing reports and dashboards to support strategic decision-making
• Developing sector performance indicators (e.g. price indices supply and demand occupancy rates etc.)
• Supporting policy development and investment planning by providing data-driven recommendations
• Contributing to real estate market studies and identifying market trends to support and update strategic urban development plans
• Collaborating with government entities and investors to provide transparent and accurate insights into the real estate market

Requirements

• Bachelors degree in Economics Statistics Business Administration Data Analysis or a related field.
• 5 years of experience in data analysis or market research with knowledge of the real estate market.
• Certifications in data analysis or economics are not required but are preferred
• Certifications in Data Analysis such as RICS or CF

We are seeking a highly skilled Data Analyst to join our team in Dubai. As a key member of our organization, you will be responsible for collecting, organizing, and analyzing data that informs business decisions.

- Collect and analyze large datasets to identify trends and patterns
- Develop and maintain databases to ensure accurate data storage and retrieval
- Create data visualizations to effectively communicate insights to stakeholders
- Collaborate with cross-functional teams to drive business growth through data-driven decision making

- Bachelor's degree in Statistics, Mathematics, Computer Science, or related field
- Proficient in database management systems such as SQL and programming languages like Python
- Excellent problem-solving skills and ability to interpret complex data quickly and accurately
- Strong communication and presentation skills, with ability to clearly convey findings to colleagues and stakeholders
- Ability to work independently and collaboratively as part of a team

- Salary of 1300 AED per month
- Opportunity to work in a dynamic and growing industry
- Chance to develop your skills and expertise in data analysis

This is an excellent opportunity for individuals who are passionate about data and its applications. If you are motivated, detail-oriented, and able to work well under pressure, we encourage you to apply.

Effective Business Decisions Using Data Analysis

ID 257

Course: Effective Business Decisions Using Data Analysis

This interactive, applications-driven 5-day course will highlight the added value that data analytics can offer a professional as a decision support tool in management decision making. It will show the use of data analytics to support strategic initiatives; to inform on policy information; and to direct operational decision making. The course will emphasize applications of data analytics in management practice; focus on the valid interpretation of data analytics findings; and create a clearer understanding of how to integrate quantitative reasoning into management decision making. Exposure to the discipline of data analytics will ultimately promote greater confidence in the use of evidence-based information to support management decision making.

• Discussions on applications of data analytics in management
• The importance of data in data analytics
• Applying data analytical methods through worked examples
• Focusing on management interpretation of statistical evidence
• How to integrate statistical thinking into the work domain

• Explain the scope and structure of data analytics.
• Apply a cross-section of useful data analytics.
• Interpret meaningfully and critically assess statistical evidence.
• Identify relevant applications of data analytics in practice.

• Professionals in management support roles
• Analysts who typically encounter data/analytical information regularly in their work environment
• Those who seek to derive greater decision-making value from data analytics

This course will utilise a variety of proven adult learning techniques to ensure maximum understanding, comprehension, and retention of the information presented. The daily workshops will be highly interactive and participative. This involves regular discussion of applications as well as hands-on exposure to data analytics techniques using Microsoft Excel. Delegates are strongly encouraged to bring and analyse data from their own work domain. This adds greater relevancy to the content. Emphasis is also placed on the valid interpretation of statistical evidence in a management context.

• Day One: Setting the Statistical Scene in Management
  • Introduction; The quantitative landscape in management
  • Thinking statistically about applications in management (identifying KPIs)
  • The integrative elements of data analytics
  • Data: The raw material of data analytics (types, quality, and data preparation)
  • Exploratory data analysis using Excel (pivot tables)
  • Using summary tables and visual displays to profile sample data
• Day Two: Evidence-based Observational Decision Making
  • Numeric descriptors to profile numeric sample data
  • Central and non-central location measures
  • Quantifying dispersion in sample data
  • Examine the distribution of numeric measures (skewness and bimodal)
  • Exploring relationships between numeric descriptors
  • Breakdown analysis of numeric measures
• Day Three: Statistical Decision Making – Drawing Inferences from Sample Data
  • The foundations of statistical inference
  • Quantifying uncertainty in data – the normal probability distribution
  • The importance of sampling in inferential analysis
  • Sampling methods (random-based sampling techniques)
  • Understanding the sampling distribution concept
  • Confidence interval estimation
• Day Four: Statistical Decision Making – Drawing Inferences from Hypotheses Testing
  • The rationale of hypotheses testing
  • The hypothesis testing process and types of errors
  • Single population tests (tests for a single mean)
  • Two independent population tests of means
  • Matched pairs test scenarios
  • Comparing means across multiple populations
• Day Five: Predictive Decision Making - Statistical Modeling and Data Mining
  • Exploiting statistical relationships to build prediction-based models
  • Model building using regression analysis
  • Model building process – the rationale and evaluation of regression models
  • Data mining overview – its evolution
  • Descriptive data mining – applications in management
  • Predictive (goal-directed) data mining – management applications

Effective Business Decisions Using Data Analysis

ID 257

Course: Effective Business Decisions Using Data Analysis

This interactive, applications-driven 5-day course will highlight the added value that data analytics can offer a professional as a decision support tool in management decision making. It will show the use of data analytics to support strategic initiatives; to inform on policy information; and to direct operational decision making. The course will emphasize applications of data analytics in management practice; focus on the valid interpretation of data analytics findings; and create a clearer understanding of how to integrate quantitative reasoning into management decision making. Exposure to the discipline of data analytics will ultimately promote greater confidence in the use of evidence-based information to support management decision making.

• Discussions on applications of data analytics in management
• The importance of data in data analytics
• Applying data analytical methods through worked examples
• Focusing on management interpretation of statistical evidence
• How to integrate statistical thinking into the work domain

• Explain the scope and structure of data analytics.
• Apply a cross-section of useful data analytics.
• Interpret meaningfully and critically assess statistical evidence.
• Identify relevant applications of data analytics in practice.

• Professionals in management support roles
• Analysts who typically encounter data/analytical information regularly in their work environment
• Those who seek to derive greater decision-making value from data analytics

This course will utilise a variety of proven adult learning techniques to ensure maximum understanding, comprehension, and retention of the information presented. The daily workshops will be highly interactive and participative. This involves regular discussion of applications as well as hands-on exposure to data analytics techniques using Microsoft Excel. Delegates are strongly encouraged to bring and analyse data from their own work domain. This adds greater relevancy to the content. Emphasis is also placed on the valid interpretation of statistical evidence in a management context.

• Day One: Setting the Statistical Scene in Management
  • Introduction; The quantitative landscape in management
  • Thinking statistically about applications in management (identifying KPIs)
  • The integrative elements of data analytics
  • Data: The raw material of data analytics (types, quality, and data preparation)
  • Exploratory data analysis using Excel (pivot tables)
  • Using summary tables and visual displays to profile sample data
• Day Two: Evidence-based Observational Decision Making
  • Numeric descriptors to profile numeric sample data
  • Central and non-central location measures
  • Quantifying dispersion in sample data
  • Examine the distribution of numeric measures (skewness and bimodal)
  • Exploring relationships between numeric descriptors
  • Breakdown analysis of numeric measures
• Day Three: Statistical Decision Making – Drawing Inferences from Sample Data
  • The foundations of statistical inference
  • Quantifying uncertainty in data – the normal probability distribution
  • The importance of sampling in inferential analysis
  • Sampling methods (random-based sampling techniques)
  • Understanding the sampling distribution concept
  • Confidence interval estimation
• Day Four: Statistical Decision Making – Drawing Inferences from Hypotheses Testing
  • The rationale of hypotheses testing
  • The hypothesis testing process and types of errors
  • Single population tests (tests for a single mean)
  • Two independent population tests of means
  • Matched pairs test scenarios
  • Comparing means across multiple populations
• Day Five: Predictive Decision Making - Statistical Modeling and Data Mining
  • Exploiting statistical relationships to build prediction-based models
  • Model building using regression analysis
  • Model building process – the rationale and evaluation of regression models
  • Data mining overview – its evolution
  • Descriptive data mining – applications in management
  • Predictive (goal-directed) data mining – management applications

This 5-day course focuses on leveraging data analytics as a decision support tool in management. Data analytics is increasingly being used by professionals to make informed business decisions.

• Explore the applications of data analytics in management practice
• Leverage statistical evidence and integrate quantitative reasoning into decision making
• Promote confidence in using evidence-based information

• Explain the scope and structure of data analytics
• Apply cross-sectional data analytics techniques
• Interpret and critically assess statistical evidence
• Identify relevant applications of data analytics in practice

• Management support professionals
• Data analysts regularly encountering data/analytical information
• Professionals seeking to derive greater decision-making value from data analytics

Key Skills:

• Ability to interpret statistical evidence
• Integration of quantitative reasoning into decision making
• Appreciation of data analytics applications in management

Benefits:

• Improved decision-making skills through data-driven insights
• Enhanced understanding of data analytics in management
• Increased confidence in using evidence-based information

Bayut & dubizzle - The Arab World's only Homegrown Unicorn Business is now inviting applications for internships of 6 months duration in our Business Intelligence department.

World-class mentors, fast paced & high performing work environment, our open culture and the opportunity to make an impact are just a few of the reasons why our internship program is highly sought after.

As a Machine Learning Intern, you will be participating in exciting projects covering the end-to-end Data Science lifecycle – from raw data cleaning and exploration with primary and third-party systems, through advanced state-of-the-art data visualization and Machine learning development.

You will work in a modern cloud-based data warehousing environment hosting Machine Learning models alongside alongside a team of diverse, intense and interesting co-workers. You will liaise with other departments – such as product & tech, the core business verticals, trust & safety, finance and others – to enable them to be successful.

In this role, you will:

• Query large datasets with SQL and feed ML models.
• Perform data exploration to find patterns in the data and understand the state and quality of the data available.
• Utilize Python code for analyzing data and building statistical models to solve specific business problems.
• Evaluate ML models and fine tune model parameters considering the business problem behind.
• Collaborate with senior peers to Deploy ML models in production.
• Build customer-facing reporting tools to provide insights and metrics which track system performance.
• Being part and contributing towards a strong team culture and ambition to be on the cutting edge of big data
• Participate in the off-hours on call stability rotation to support live ML models
• Bachelor's degree in AI, Statistics, Math, Operations Research, Engineering, Computer Science, or a related quantitative field.
• Statistical modelling and math
• Basic knowledge of Machine learning algorithms.
• Basic knowledge of SQL.
• Basic knowledge of visualization tools such as Periscope
• Excellent verbal and written communication.
• Strong problem solving skills.
• Ability to contribute to a platform used by more than 5M users in UAE and other platforms in the region.
• Strengthen your resume and build your network.
• Opportunity to find a full time career with the region's leading organization.
• Working in a multicultural environment with over 50 different nationalities
• Access to the Learning & Development tools and courses provided by the company.

Bayut & dubizzle is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

#dubizzle

#J-18808-Ljbffr

About the opportunity

The Growth Data Science Director will lead and accelerate growth strategies across marketing optimization, product initiatives (e.g., churn reduction), ecosystem plays, and ads revenue for talabat and Quick commerce. This role requires collaboration with product teams, senior marketing, and partner leadership to drive data-driven decisions and business growth.

What’s On Your Plate?

• Data Science: Define and execute data strategies aligned with company goals, leading cross-functional teams to develop innovative data-driven products and insights that impact business outcomes.
• Strategic Leadership and Business Acumen: Demonstrate strategic leadership, articulate vision, and incorporate emerging trends and technologies to add value.
• Collaboration and Influence: Work with stakeholders including executives, product managers, engineers, and external partners; represent the organization at industry events.
• Resource Management: Manage teams, budgets, and resources; prioritize projects; coach and mentor managers.
• Strategic Hiring and Talent Development: Recruit, develop career paths, and provide feedback to team members.
• Organizational Change Management: Lead change initiatives, communicate effectively, and manage resistance.

What you need to be successful

• 8-10+ years in data science, with leadership experience in managing teams or consulting.
• Ph.D. or Master’s in relevant fields such as computer science, statistics, or data science.
• Proven leadership in fostering high-performance teams and applying data science skills to create impactful products and insights.
• Deep knowledge of statistics, causality, experimentation, and modeling.
• Business acumen with experience in quantifying impact through data initiatives.
• Strategic thinking and ability to develop data-driven roadmaps aligned with organizational goals.

Who we are

Since 2004, talabat has been Kuwait’s leading on-demand food and Q-commerce app, serving eight countries. We leverage technology to simplify life, optimize operations, and provide earning opportunities. We foster a high-performance culture, value authenticity, and are proud of our awards and diverse team of over 6,000 Talabaty committed to making a difference.

Property Finder is the leading property portal in the Middle East and North Africa (MENA) region, dedicated to shaping an inclusive future for real estate while spearheading the region’s growing tech ecosystem. At its core is a clear and powerful purpose: To change living for good in the region.

Founded on the value of great ambitions, Property Finder connects millions of property seekers with thousands of real estate professionals every day. The platform offers a seamless and enriching experience, empowering both buyers and renters to make informed decisions. Since its inception in 2007, Property Finder has evolved into a trusted partner for developers, brokers, and home seekers. As a lighthouse tech company, it continues to create an environment where people can thrive and contribute meaningfully to the transformation of real estate in MENA.

Reports To

Head of AI & Data Science

Summary

We are looking for a skilled professional with expertise in Machine Learning Engineering (MLE Level II) and Data Science (Level II) to join our AI & Data Science team at Property Finder. This role will focus on developing and deploying advanced AI solutions using Generative AI , Large Language Models (LLMs) , and transformer-based models to drive personalisation, automation and innovation across our platforms. The ideal candidate will have a strong foundation in machine learning, practical experience with transformer architectures, and the ability to collaborate across teams to deliver impactful AI-driven solutions. Join a forward-thinking team at the forefront of innovation, working on cutting-edge projects that leverage GenAI and Large Language Models (LLMs) to transform the real estate experience. Be part of a collaborative and dynamic environment that values continuous learning, technical excellence, and teamwork. With exposure to advanced AI technologies and challenging projects, you'll have the opportunity to grow professionally and make a meaningful impact in an ever-evolving industry.

• Deep understanding and working knowledge of deep learning and neural networks architectures
• Design, fine-tune, and deploy Large Language Models (LLMs) and generative models tailored to business needs.
• Develop applications using transformer-based architectures such as GPT, BERT, T5, or similar frameworks.
• Implement use cases in personalization, content creation, and workflow automation using Generative AI.
• Optimize LLM performance for inference in real-time or large-scale production environments.
• Conduct research and experimentation to identify improvements in GenAI and LLM applications.
• Build and maintain scalable ML pipelines to deploy LLMs and generative models efficiently.
• Develop workflows for fine-tuning and serving transformer models in production.
• Automate the deployment process using MLOps tools (e.g., Kubernetes, MLflow, Docker).
• Optimize data pipelines and feature engineering processes to support transformer-based models.
• Build and implement ML models for predictive analysis and personalization.
• Collaborate with cross-functional teams to generate actionable insights and support business strategies.
• Conduct data wrangling, feature engineering, and advanced statistical analysis.
• Design and evaluate experiments (e.g., A/B testing) to validate model performance.
• Partner with the GenAI team to align model development with business goals.
• Work closely with MLE and Data Science teams to ensure seamless integration of LLMs and generative solutions into production workflows.
• Collaborate with the Futurism team to explore cutting-edge AI applications and opportunities for LLMs.

The Person

Desired Qualifications

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field.
• 5+ years of experience in machine learning engineering and data science roles, with 2+ years hands-on experience in transformers, LLMs and generative models.
• Proficiency in fine-tuning and deploying transformer models (e.g., GPT, BERT, T5).
• Familiarity with tools like Hugging Face Transformers, OpenAI APIs, and LangChain.
• Expertise in prompt engineering and domain-specific fine-tuning of LLMs.
• Knowledge of attention mechanisms and sequence-to-sequence modeling.
• Strong experience with ML pipelines and MLOps tools (e.g., Kubernetes, Docker, MLflow).
• Advanced SQL and Python programming skills.
• Familiarity with cloud platforms (AWS, GCP, Azure) for scalable deployments.
• Intermediate experience with supervised and unsupervised learning algorithms.
• Proficiency in data wrangling and feature engineering.
• Knowledge of statistical analysis and hypothesis testing.
• Strong analytical and problem-solving abilities.
• Effective communication skills to collaborate with cross-functional teams.
• Adaptability to work in a fast-paced, dynamic environment.

Our promise to talent

At Property Finder, we believe talent thrives in an environment where you can be your best self. Where you are empowered to create, elevate, grow, and care. Our team is made up of the best and brightest, united by a shared ambition to change living for good in the region. We attract top talent who want to make an impact. We firmly believe that when our people grow, we all succeed.

Property Finder Guiding Principles

• Think Future First
• Data Beats Opinions, Speed Beats Perfection
• Our People, Our Power
• The Biggest Risk is Taking no Risk at All

Messilat is seeking a talented MLOps Engineer for our client's team. The perfect candidate will excel in deploying models, managing microservices, utilizing Docker, and maintaining Kubernetes. Our client is also exploring AI applications in customer service, cybersecurity, and compliance.

Key Responsibilities

• Transition models from development to production, ensuring scalability and high performance. Collaborate with development teams for seamless model deployment.
• Implement monitoring and maintenance strategies for deployed models to ensure ongoing accuracy and reliability.
• Maintain Kubernetes clusters to ensure high availability and performance.
• Work with cross-functional teams to understand business requirements and deliver effective machine learning solutions.
• Develop and implement strategies that optimize efficiency and data quality.

Qualifications

• Minimum of 3 years of experience in MLOps or a related field.
• Expertise in model deployment, containerization, and orchestration (e.g., Docker, Kubernetes).
• Familiarity with cloud platforms (e.g., AWS, or on-premises) for model deployment and management.
• Experience in deploying AI models and managing their lifecycle.
• Proficiency in Python for scripting and automation.
• Prior experience in addressing scalability and pricing concerns in ML operations.

Skills

• Model deployment, monitoring, Docker, Kubernetes, AWS Cloud services, on-premises deployment, collaboration.

If you are passionate about MLOps and looking for a challenging role where you can make a significant impact, we would love to hear from you. Apply today!