67 Data Analysis jobs in Dubai

We are seeking a highly motivated and detail-oriented Pharmaceutical Data Analyst to join our dynamic team. As a key member of the Commercial Operations department, you will have the opportunity to contribute to the transformation, harmonization, and continuous improvement of analysis and insights creation by leveraging innovative data platforms to deliver actionable insights to the business.

• Develop and implement data analytics solutions to drive business decisions
• Analyze complex data sets to identify trends and opportunities for growth
• Collaborate with cross-functional teams to integrate data insights into business strategies

• Bachelor's degree in Data Science, Statistics, or related field
• Proficiency in data analysis tools and technologies such as Excel, SQL, and Tableau
• Strong analytical and problem-solving skills

• A competitive salary and benefits package
• Ongoing training and development opportunities
• A collaborative and dynamic work environment

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

• Bachelor's 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

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

Role Summary

As the leading delivery company in the region we have a great responsibility and opportunity to impact the lives of millions of customers restaurant partners and riders. To realize our potential we need to advance our platform to become much more intelligent in how it understands and serves our users.
As a data scientist on the analysis track your mission will be to improve the quality of the decisions made across product and business via relevant reliable and actionable data. You will own a particular domain across product and business and will work closely with the corresponding product and business managers as part of a talented team of data scientists and data engineers. You will own the entire data value chain including logging data modeling analysis reporting and experimentation.

Whats On Your Plate

• Leveraging ambiguous business problems as opportunities to drive objective criteria using data.
• Developing a deep understanding of the product experiences and business processes that make up your area of focus.
• Developing a deep familiarity with the source data and its generating systems through documentation interacting with the engineering teams and systematic data profiling.
• Contributing heavily to the design and maintenance of the data models that allow us to measure performance and comprehend performance drivers for your area of focus.
• Working closely with product and business teams to identify important questions that can be answered effectively with data.
• Delivering wellformed relevant reliable and actionable insights and recommendations to support datadriven decision making through deep analysis and automated reports.
• Designing planning and analyzing experiments (A/B and multivariate tests).
• Supporting product and business managers with KPI design and goal setting.
• Mentoring other data scientists in their growth journeys.
• Contributing to improving our ways of work our tooling and our internal training programs.

Qualifications :

What Did We Order
Technical Experience

• Excellent SQL.
• Competence with reproducible data analysis using Python or R.
• Familiarity with data modeling and dimensional design.
• Strong command over the entire data analysis lifecycle including; problem formulation data auditing rigorous analysis interpretation recommendations and presentation.
• Familiarity with different types of analysis including; descriptive exploratory inferential causal and predictive analysis.
• Deep understanding of the various experiment design and analysis workflows and the corresponding statistical techniques.
• Familiarity with product data (impressions events .) and product health measurement (conversion engagement retention .).
• Familiarity with BigQuery and the Google Cloud Platform is a plus.
• Data engineering and data pipeline development experience (e.g. via Airflow) is a plus.
• Experience with classical ML frameworks (e.g. Scikitlearn XGBoost LightGBM .) is a plus.

Qualifications

• Bachelors degree in engineering computer science technology or similar fields. A postgraduate degree is a plus but not required.
• 5 years of overall experience working in data science and machine learning.
• Experience doing data science in an online consumer product setting is a plus.
• A good problem solver with a figure it out growth mindset.
• An excellent collaborator.
• An excellent communicator.
• A strong sense of ownership and accountability.
• A keep it simple approach to #makeithappen.

Remote Work :

No

Employment Type :

Fulltime

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.

Role Summary

As the leading delivery company in the region we have a great responsibility and opportunity to impact the lives of millions of customers, restaurant partners, and riders. To realize our potential we need to advance our platform to become much more intelligent in how it understands and serves our users.
As a data scientist on the analysis track your mission will be to improve the quality of the decisions made across product and business via relevant, reliable, and actionable data. You will own a particular domain across product and business and will work closely with the corresponding product and business managers as part of a talented team of data scientists and data engineers. You will own the entire data value chain including logging, data modeling, analysis, reporting, and experimentation.

Whats On Your Plate

• Leveraging ambiguous business problems as opportunities to drive objective criteria using data.
• Developing a deep understanding of the product experiences and business processes that make up your area of focus.
• Developing a deep familiarity with the source data and its generating systems through documentation, interacting with the engineering teams, and systematic data profiling.
• Contributing heavily to the design and maintenance of the data models that allow us to measure performance and comprehend performance drivers for your area of focus.
• Working closely with product and business teams to identify important questions that can be answered effectively with data.
• Delivering well-formed, relevant, reliable, and actionable insights and recommendations to support data-driven decision making through deep analysis and automated reports.
• Designing, planning, and analyzing experiments (A/B and multivariate tests).
• Supporting product and business managers with KPI design and goal setting.
• Mentoring other data scientists in their growth journeys.
• Contributing to improving our ways of work, our tooling, and our internal training programs.

What Did We Order
Technical Experience

• Excellent SQL.
• Competence with reproducible data analysis using Python or R.
• Familiarity with data modeling and dimensional design.
• Strong command over the entire data analysis lifecycle including problem formulation, data auditing, rigorous analysis, interpretation, recommendations, and presentation.
• Familiarity with different types of analysis including descriptive, exploratory, inferential, causal, and predictive analysis.
• Deep understanding of the various experiment design and analysis workflows and the corresponding statistical techniques.
• Familiarity with product data (impressions, events) and product health measurement (conversion, engagement, retention).
• Familiarity with BigQuery and the Google Cloud Platform is a plus.
• Data engineering and data pipeline development experience (e.g. via Airflow) is a plus.
• Experience with classical ML frameworks (e.g. Scikit-learn, XGBoost, LightGBM) is a plus.

Qualifications:

• Bachelor's degree in engineering, computer science, technology, or similar fields. A postgraduate degree is a plus but not required.
• 3 years of overall experience working in data science and machine learning.
• Experience doing data science in an online consumer product setting is a plus.
• A good problem solver with a figure-it-out growth mindset.
• An excellent collaborator.
• An excellent communicator.
• A strong sense of ownership and accountability.
• A keep-it-simple approach to #makeithappen.

Remote Work:

No

Employment Type:

Full-time

As the leading delivery company in the region, we have a great responsibility and opportunity to impact the lives of millions of customers, restaurant partners, and riders. To realize our potential, we need to advance our platform to become much more intelligent in how it understands and serves our users.

As a data scientist on the analysis track, your mission will be to improve the quality of decisions made across product and business through relevant, reliable, and actionable data. You will own a specific domain within product and business, working closely with managers and a team of data scientists and engineers. Your responsibilities include managing the entire data value chain: logging, data modeling, analysis, reporting, and experimentation.

• Transform ambiguous business problems into data-driven objectives.
• Develop a deep understanding of your focus area’s product experiences and business processes.
• Familiarize yourself with source data and systems through documentation, collaboration with engineering teams, and data profiling.
• Contribute to designing and maintaining data models to measure performance and identify drivers.
• Collaborate with product and business teams to formulate key questions for data analysis.
• Deliver relevant and actionable insights via analysis and automated reports.
• Design and analyze experiments (A/B and multivariate tests).
• Support KPI development and goal setting for product and business managers.
• Mentor fellow data scientists.
• Help improve workflows, tools, and training programs.

Technical Experience

• Proficiency in reproducible data analysis using Python or R.
• Experience with data modeling and dimensional design.
• Strong command over the full data analysis lifecycle: problem formulation, auditing, analysis, interpretation, and presentation.
• Knowledge of various analysis types: descriptive, exploratory, inferential, causal, and predictive.
• Understanding of experiment design and statistical techniques.
• Familiarity with product data (impressions, events, etc.) and health metrics (conversion, engagement, retention).
• Experience with BigQuery and Google Cloud Platform is a plus.
• Experience with data pipelines (e.g., Airflow) and ML frameworks (e.g., Scikit-learn, XGBoost, LightGBM) is a plus.

Qualifications

• Bachelor’s degree in engineering, computer science, or related fields; postgraduate degree is a plus.
• At least 3 years of experience in data science and machine learning.
• Experience in online consumer products is a plus.
• Strong problem-solving skills and growth mindset.
• Ownership and accountability mindset.
• Practical, straightforward approach to execution (#makeithappen).

Disclaimer: Naukrigulf.com is a platform connecting jobseekers and employers. Candidates should verify employer credentials independently. We do NOT endorse requests for money or sharing personal/bank details. For security advice, visit our site. Report fraud to

Role Summary

As the leading delivery company in the region we have a great responsibility and opportunity to impact the lives of millions of customers, restaurant partners, and riders. To realize our potential we need to advance our platform to become much more intelligent in how it understands and serves our users.
As a data scientist on the analysis track your mission will be to improve the quality of the decisions made across product and business via relevant, reliable, and actionable data. You will own a particular domain across product and business and will work closely with the corresponding product and business managers as part of a talented team of data scientists and data engineers. You will own the entire data value chain including logging, data modeling, analysis, reporting, and experimentation.

Whats On Your Plate

• Leveraging ambiguous business problems as opportunities to drive objective criteria using data.
• Developing a deep understanding of the product experiences and business processes that make up your area of focus.
• Developing a deep familiarity with the source data and its generating systems through documentation, interacting with the engineering teams, and systematic data profiling.
• Contributing heavily to the design and maintenance of the data models that allow us to measure performance and comprehend performance drivers for your area of focus.
• Working closely with product and business teams to identify important questions that can be answered effectively with data.
• Delivering well-formed, relevant, reliable, and actionable insights and recommendations to support data-driven decision making through deep analysis and automated reports.
• Designing, planning, and analyzing experiments (A/B and multivariate tests).
• Supporting product and business managers with KPI design and goal setting.
• Mentoring other data scientists in their growth journeys.
• Contributing to improving our ways of work, our tooling, and our internal training programs.

What Did We Order
Technical Experience

• Excellent SQL.
• Competence with reproducible data analysis using Python or R.
• Familiarity with data modeling and dimensional design.
• Strong command over the entire data analysis lifecycle including problem formulation, data auditing, rigorous analysis, interpretation, recommendations, and presentation.
• Familiarity with different types of analysis including descriptive, exploratory, inferential, causal, and predictive analysis.
• Deep understanding of the various experiment design and analysis workflows and the corresponding statistical techniques.
• Familiarity with product data (impressions, events) and product health measurement (conversion, engagement, retention).
• Familiarity with BigQuery and the Google Cloud Platform is a plus.
• Data engineering and data pipeline development experience (e.g. via Airflow) is a plus.
• Experience with classical ML frameworks (e.g. Scikit-learn, XGBoost, LightGBM) is a plus.

Qualifications:

• Bachelor's degree in engineering, computer science, technology, or similar fields. A postgraduate degree is a plus but not required.
• 3 years of overall experience working in data science and machine learning.
• Experience doing data science in an online consumer product setting is a plus.
• A good problem solver with a figure-it-out growth mindset.
• An excellent collaborator.
• An excellent communicator.
• A strong sense of ownership and accountability.
• A keep-it-simple approach to #makeithappen.

Remote Work:

No

Employment Type:

Full-time