4 Research Associate jobs in the United Arab Emirates

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

Organisation/Company NEW YORK UNIVERSITY ABU DHABI Research Field Computer science Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Country United Arab Emirates Application Deadline 5 Nov 2025 - 00:00 (UTC) Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Description

The laboratory of Dr. Djellel Difallah in the Division of Science, New York University Abu Dhabi, seeks a Post-Doctoral Associate or a Research Associate to join a lab focused on applied machine learning.

The successful applicant will participate in research involving human computation, knowledge discovery, machine learning, and data science. The position will provide the opportunity to develop applied research skills in machine learning, interact with an international network of collaborators, and gain post-doctoral research experience.

The ideal candidate is self-motivated and can work independently, has a passion for AI and its applications, and is willing to learn new technologies. The candidate should have a PhD in Computer Science or a closely related field. Relevant background and skills include:

• Strong foundation in one of the following areas: Machine Learning / Information Retrieval / Knowledge Graph Representation / Recommender Systems
• Graph Theory/Network Science
• Python, and up-to-date machine learning libraries
• Excellent written and verbal communication skills
• Track record of publishing in top tier conferences

For consideration, applicants need to submit a cover letter, curriculum vitae, list of publications (if applicable), a 1-page statement of research interests, and three letters of reference. If you have any questions, please email:

About NYUAD:

NYU Abu Dhabi is a degree-granting research university with a fully integrated liberal arts and science undergraduate program in the Arts, Sciences, Social Sciences, Humanities, and Engineering. NYU Abu Dhabi, NYU New York, and NYU Shanghai, form the backbone of NYU’s global network university, an interconnected network of portal campuses and academic centers across six continents that enable seamless international mobility of students and faculty in their pursuit of academic and scholarly activity. This global university represents a transformative shift in higher education, one in which the intellectual and creative endeavors of academia are shaped and examined through an international and multicultural perspective. As a major intellectual hub at the crossroads of the Arab world, NYUAD serves as a center for scholarly thought, advanced research, knowledge creation, and sharing, through its academic, research, and creative activities.

EOE/AA/Minorities/Females/Vet/Disabled/Sexual Orientation/Gender Identity Employer

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.