7 Financial Data jobs in the United Arab Emirates

Location: Dubai

The Market Research Analyst will be responsible for gathering, analyzing, and interpreting market data to help the company make informed business decisions. The role requires strong analytical skills, attention to detail, and the ability to translate complex data into actionable insights. The ideal candidate will work closely with management, marketing, and product teams to identify market trends, customer preferences, and competitive positioning in the region.

Collect and analyze data on consumers, competitors, and market conditions using statistical tools and software.

Conduct surveys, interviews, focus groups, and other methods to gather market intelligence.

Interpret data, identify trends, and prepare detailed reports with actionable insights and recommendations.

Monitor and forecast marketing and sales trends to support strategic planning.

Evaluate the effectiveness of marketing campaigns and suggest improvements.

Develop and maintain databases of market data, research findings, and competitor information.

Present research findings to management and stakeholders through charts, reports, and presentations.

Collaborate with cross-functional teams including marketing, sales, product development, and strategy.

Stay updated with industry developments, economic indicators, and emerging trends in the Middle East market.

Bachelor’s degree in Marketing, Business Administration, Economics, Statistics, or a related field (Master’s degree preferred).

2–4 years of experience in market research, data analytics, or a related field.

Proven track record of conducting both qualitative and quantitative research.

Strong knowledge of research methodologies, data collection, and statistical analysis techniques.

Proficiency in market research and statistical tools (SPSS, SAS, R, Python, or similar).

Strong skills in data visualization (Excel, Power BI, Tableau, or equivalent).

Excellent analytical, problem-solving, and critical thinking abilities.

Strong communication and presentation skills for conveying findings to non-technical stakeholders.

Ability to handle multiple projects, prioritize tasks, and meet deadlines.

High attention to detail with a commitment to accuracy and quality.

Familiarity with Middle Eastern market dynamics will be an added advantage.

Antal Motor City is partnering with a fast-growing financial services leader in Dubai that is making significant investments in Generative AI and advanced data science. This is a unique chance to join a team at the forefront of AI-driven transformation in the region.

As a Data Scientist, you’ll work on high-impact initiatives such as fraud detection, credit risk, financial forecasting, and personalised digital services.

You’ll be applying deep learning, large language models and advanced analytics to solve some of the most pressing challenges in financial services.

• Design, build and deploy AI/ML models to address fraud, risk and forecasting.
• Collaborate with product, risk, and technology teams to translate business needs into scalable AI solutions.
• Research, test and optimise algorithms, including deep learning and LLMs.
• Present insights and recommendations directly to senior stakeholders.

• Bachelor’s or Master’s in Computer Science, Data Science, or related field.
• Hands-on experience developing and deploying AI/ML models into production.
• Strong Python skills with frameworks such as TensorFlow, PyTorch, or Keras.
• Deep understanding of algorithms, data structures, and machine learning techniques.
• (Preferred) Exposure to financial services or fintech environments.

• Opportunity to work at the cutting edge of Generative AI in financial services.
• Large-scale projects with immediate, real-world business impact.
• Competitive salary and benefits package.

We are an equal opportunities employer and welcome applications from all qualified candidates. By submitting your application, you grant us written consent to keep your resume in our system. We appreciate your interest in working with us and thank you for applying.

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

Overview

Job Title: Business Data Analyst (Banking) - Digital Transformation

Job Type: Full-Time Contract (1 year, renewable)

Location: On-site, Dubai, Dubai, United Arab Emirates

Job Summary:

Join our team as a Business Data Analyst at the forefront of digital transformation within a leading UAE bank. In this pivotal role, you will bridge business stakeholders and technology teams, applying deep analytical expertise to drive insights, optimize processes, and elevate customer journeys across key digital banking initiatives. Embrace an asynchronous work culture that values exceptional written communication and proactive problem-solving.

• Elicit, analyze, and document business requirements, user stories, and process flows for digital projects.
• Act as a key liaison between business units and technical teams to ensure clear understanding of project objectives.
• Conduct gap analysis and impact assessments for new features and system changes within core banking functions.
• Participate in Agile/Scrum ceremonies, including sprint planning, backlog grooming, and daily stand-ups.
• Design and execute test scenarios, supporting user acceptance testing (UAT) and solution validation.
• Write complex SQL queries to extract and analyze large datasets, generating actionable insights and KPI reports with Power BI.
• Translate analytical findings into clear, data-driven recommendations and presentations for diverse stakeholders.

• Bachelor’s degree in Computer Science, Engineering, Finance, Business, or a quantitative discipline.
• 5-9 years’ experience as a Business Analyst, with a strong background in Banking, Financial Services, or FinTech.
• High proficiency in SQL and PL/SQL, with hands-on experience in Power BI for data visualization.
• Proven experience working with core-banking systems and exposure to digital transformation projects.
• Solid understanding of Agile methodologies (Scrum, Kanban) and expertise with JIRA.
• Exceptional written communication skills, adept at working in asynchronous, collaborative environments.
• Strong analytical and critical thinking abilities with excellent stakeholder management.

• Relevant professional certifications (CBAP, PMI-PBA, Agile Scrum, Data Analytics).
• Experience with data modeling, Python or R for advanced analytics, and systems like Flexcube or OFSAA.
• Expertise in digital banking products, customer journey mapping, and process optimization.

Become part of our team and contribute to high-impact initiatives, working on projects that set industry standards and drive meaningful change. We foster an inclusive, high-performing culture offering career development opportunities, comprehensive benefits, and a collaborative environment to help you thrive.