366 Data Analysis jobs in Dubai Silicon Oasis

Data Analysis Opportunity

• Collect, examine, and interpret data to reveal trends and patterns.
• Create clear visual reports based on data analysis and provide actionable recommendations.
• Work with large datasets using statistical methods.

• Degree or equivalent in a related field.
• Previous experience as a Data Analyst is preferred but not essential.
• Proficiency in Microsoft Office (especially Excel) and programming languages such as SQL, R, or Python is advantageous.

Benefits:

Join our team and develop your skills while contributing to our success.

We are seeking a skilled Quantitative Researcher to join our team.

• Our ideal candidate will have extensive experience in market research and strategy implementation, as well as strong analytical and problem-solving skills.

Key Responsibilities:

• Conduct comprehensive market research to identify new trading opportunities;
• Design and implement effective trading strategies based on market research findings;
• Perform thorough analysis of existing trading strategies to enhance risk-adjusted returns and adapt to changing market conditions;
• Collaborate with colleagues to leverage diverse expertise in refining and optimizing strategies.

Qualifications:

• 2+ years of experience in quantitative research/trading with specialization in spreads/option strategies;
• Experience with market-making trading strategies;
• Strong knowledge of statistical analysis, data mining, and predictive modeling;
• Understanding of market microstructure;
• Experience with using L2/L3 market data for research;
• Exceptional quantitative and mathematical skills, adept problem-solving;
• Proficient in any research-oriented programming language (Python preferred) with ability to write efficient code;
• Strong collaborative spirit, work ethics, and determined drive for success, with ability to work both independently and as part of a team;
• Strong communication skills with ability to clearly explain complex ideas.

Preferred Qualifications:

• Advanced degree (PhD or Masters) in a quantitative discipline such as Computer Science, Statistics, Mathematics, Physics, or a related field;
• Good programming skills in programming languages like C/C++ or Rust.

Benefits:

• Full-time employment opportunity;
• Remote work option available;
• Potential for career growth and professional development.

Contact Information:

Ajman UAE is looking for a skilled Data Analyst to join our team. Our ideal candidate will possess a strong background in data analysis and excellent analytical, problem-solving and communication skills.

The successful applicant will be responsible for collecting, analyzing, and interpreting large amounts of data using various tools and techniques. They will also develop data models and utilize data visualization tools to help interpret data efficiently.

This role requires exceptional knowledge of SQL and other programming languages such as Python or R. The ideal candidate should have a Bachelor's Degree in Computer Science, Mathematics, or similar field.

We are seeking someone with proven experience in data analysis who can work independently and collaboratively within a team environment. Excellent communication skills, both written and verbal, are essential for this position.

The successful candidate will be able to manage multiple projects simultaneously while meeting tight deadlines and demonstrate strong attention to detail with excellent organizational skills.

• Key Responsibilities:
• Collecting, analyzing, and interpreting large amounts of data.
• Developing data models and utilizing data visualization tools.
• Maintaining accurate and up-to-date records.

Requirements:

• Bachelor's Degree in Computer Science, Mathematics, or similar field.
• Proven experience in data analysis.
• Excellent knowledge of SQL and other programming languages.
• Ability to work independently and collaboratively within a team environment.
• Excellent communication skills, both written and verbal.
• Ability to manage multiple projects simultaneously while meeting tight deadlines.
• Strong attention to detail with excellent organizational skills.

What We Offer:

A competitive salary package and opportunities for career growth and development in a dynamic and innovative organization.

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.

We are seeking a seasoned Data Analyst with expertise in data analysis and interpretation. The ideal candidate will possess 2+ years of experience in analyzing complex datasets and generating actionable reports.

• Analyze and interpret large datasets to inform strategic business decisions.
• Utilize software programs such as Microsoft Excel and Tableau for data visualization and reporting.

• 2+ years of experience in data analysis and interpretation.
• Excellent problem-solving, communication, and analytical skills.
• Ability to work under pressure and meet tight deadlines.

Job Title: Business Insights Specialist

We are seeking a skilled professional to join our organization as a Business Insights Specialist. In this role, you will play a key part in helping us drive data-driven decision making across the business.

Your Key Responsibilities:

• Collaborate with stakeholders to identify business opportunities and develop innovative solutions using data analytics.
• Design and implement algorithms and experiments to extract insights from complex data sets, providing actionable recommendations to enhance business performance.
• Apply machine learning techniques and statistical models to solve real-world problems, driving business growth and improvement.
• Maintain effective communication with colleagues and stakeholders to ensure data needs are understood and results are effectively presented.
• Develop compelling reports that tell engaging stories about business performance, highlighting areas for improvement and opportunities for growth.
• Assess data quality and recommend improvements to data collection methods, ensuring high-quality insights are available to inform business decisions.
• Stay up-to-date with the latest advancements in data science and analytics, applying new techniques and tools to drive business success.
• Conduct research to develop prototypes and proof-of-concepts, demonstrating the potential for new ideas and approaches.

Requirements:

• Experience with statistical and data mining techniques using Python, R, SQL.
• Experience with advanced Machine Learning techniques such as Neural networks, supervised and unsupervised ML, computer vision, and image processing, text analysis.
• Experience with big data analysis and management, distributed computing tools like Hadoop, Hive, Spark.
• Experience with programming languages like C, C#, Java.
• Experience with web development frameworks like JavaScript, React, node.js.
• Experience analyzing data from third-party providers like Google Analytics, Site Catalyst, Facebook Insights.
• Experience visualizing/presenting data for stakeholders using Periscope, Business Objects, D3, ggplot, etc.
• Experience working with data architectures.

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