64 Data Science Manager jobs in Dubai

As a Data Science Manager (Customer) at Al-Futtaim Automotive, you will lead customer-centric AI and advanced analytics initiatives that drive marketing performance and enhance the customer experience. This role focuses on building predictive models, customer insights, and hyper-personalization solutions that create measurable business impact.

• Build and deploy predictive models such as lead scoring, precision marketing, marketing attribution, customer segmentation, and hyper-personalization.
• Standardize and optimize marketing performance KPIs across brands using advanced statistical methods and data visualization.
• Deliver actionable customer insights through cohort analysis, customer lifetime value modelling, churn prediction, and campaign performance analysis.
• Collaborate with stakeholders to identify, define, and prioritize high-impact use cases supported by data-driven business cases.
• Lead and mentor a team of data scientists to deliver scalable AI / ML solutions that directly improve marketing ROI and customer experience.

• Advanced knowledge of ensemble methods, gradient boosting, uplift modelling, and multi-arm bandits for marketing optimization.
• Expertise in attribution models (Markov chains, Shapley values), clustering techniques, recommender systems, and hyper-personalization.
• Proficiency in NLP, reinforcement learning, cohort analysis, lifetime value modelling, and churn prediction.
• Strong problem-solving skills and the ability to translate complex data science solutions into business value.
• Excellent stakeholder management, communication, and project management capabilities.

You will report to the Head of Customer Data Science and lead a small team comprising a Data Science Manager and Data Scientists. The team plays a pivotal role within the Finance Department at Al-Futtaim Automotive, driving innovation in customer analytics and marketing science.

• Bachelor's, Master's, or PhD in Marketing Analytics, Computer Science, or a related field.
• 7+ years of experience in customer analytics, data science, and AI / ML solutions.
• Hands-on expertise with machine learning techniques including CNNs, RNNs, LSTMs, time series forecasting, survival analysis, graph neural networks, Bayesian and causal modelling.
• Skilled in Python, SQL, Databricks, and modern ML libraries (scikit-learn, TensorFlow, PyTorch, Prophet, ARIMA / SARIMA).
• Proficient in CRM systems, marketing analytics platforms, and MLOps practices.
• Experience with LLMs, RAGs, and modern AI-based recommendation systems.
• Strong leadership skills to manage and mentor a data science team.

Embark on a journey where your unique contributions are celebrated, and your professional growth is embraced. At ADCB, we nurture a diverse, inclusive community where every voice is valued.

About the business area

GBS is a group of highly skilled and talented professionals who form an essential part of ADCB's continued journey of success. With a proud history of commitment, innovation and delivery, GBS constantly strives for excellence whilst ensuring the highest standards of quality and risk awareness. Each and every member of the GBS family plays an integral role in driving ADCB's strategy, growth and digital evolution by working closely with our valued business partners to achieve exceptional customer experience through our outstanding service and support.

We are actively seeking an ambitious professional to join our team at ADCB to work alongside passionate colleagues who share your ambition to redefine excellence in UAE banking.

In this role, your key responsibilities include:

• Analyze complex banking products and services for strategic decision-making.
• Leverage Artificial Intelligence (AI)/Machine Learning (ML) to enhance core banking processes and digital journeys.
• Leads banking analytics projects to drive business growth and innovation.
• Design and deploy advanced AI/ML models to optimize banking operations and customer journeys
• Build scalable ML pipelines using Azure AI Foundry or AWS SageMaker
• Fine-tune generative AI models using Low-Rank Adaptation (LoRA), Parameter-efficient fine-tuning (PEFT), and multimodal architectures
• Conduct A/B testing and statistical inference.
• Collaborate with engineers for containerized Continuous Integration (CI)/ Continuous Deployment (CD)
• Monitor model performance using Machine Learning Operations (MLOps) and Large Language Models (LLM) metrics Bilingual Evaluation Understudy (BLEU), ROUG.

The ideal candidate should have the following experience:

• Minimum 7 years of experience in data science, with proven expertise in AI/ML, statistical modeling, and cloud-based deployments
• Strong background in banking processes, operations, terminologies, generative AI, and Machine Learning Operations (MLOps) is essential
• Experience leading cross-functional teams and delivering scalable, production-grade solutions in a regulated, data-driven environment is highly preferred
• Bachelor's degree in quantitative field such as Computer Science, Artificial Intelligence, Software Engineering, Statistics, Mathematics or related field required
• Master's degree or equivalent in AI/ML is preferred
• Advanced Python and ML Development
• Statistical Analysis and Experimentation
• Machine Learning and MLOps
• Generative AI and LLMs
• Cloud-based ML Pipelines (Azure/AWS)
• Data Visualization and Dashboarding
• DevOps & CI/CD Practices

What we offer:

Comprehensive Benefits Package: This includes market-leading medical insurance, group life and personal accident insurance, paid leave and leave airfare, employee preferential rates on loans and finance facilities, staff discounts and offers, and children education assistance (for certain job levels).

Flexible and Remote Working Options: We understand the importance of work-life balance and offer flexible working arrangements, subject to eligibility and job requirements.

Learning and Development Opportunities: We value and facilitate continuous learning and personal development, through a variety of exciting learning opportunities, such as structured instructor-led courses, a comprehensive e-Learning catalog, on-the-job training and professional development programs.

At ADCB, we are dedicated to creating a respectful, caring and disciplined work environment that aligns with your career ambitions.

Job Title: Data Expert

At our organization, we are seeking a talented Data Expert to join our team. As a key member of our data science team, you will play a vital role in helping us make informed decisions by leveraging data insights and analysis.

Key Responsibilities:

• Design and implement advanced statistical models to analyze complex data sets.
• Develop and maintain databases to store and manage large datasets.
• Collaborate with cross-functional teams to identify business needs and develop solutions.
• Communicate findings and insights effectively to both technical and non-technical stakeholders through reports, presentations, and visualizations.
• Stay up-to-date with the latest advancements in data science, machine learning, and AI technologies.

Required Skills and Qualifications:

• Bachelor's degree in statistics, applied mathematics, or related discipline.
• Minimum of 4-6 years of experience in a similar role.
• Proficiency with data mining, mathematics, and statistical analysis.
• Advanced pattern recognition and predictive modeling experience.
• Experience with Excel, Tableau/Looker Studio, SQL, and programming languages such as Python and R.
• Storytelling and data visualization skills.
• Comfort working in a dynamic, data-oriented team with several ongoing concurrent projects.

Preferred Qualifications:

• Master's degree in stats, applied math, or related discipline.
• Experience with NoSQL, Pig, Hive, and PySpark/Big Query.

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.

Job Title: Data Analytics and Strategy Manager Carat

Work Location: Dubai UAE

Job Level: Manager

Job Type: Fulltime (Hybrid)

About Dentsu International:

Dentsu is one of the largest marketing and advertising groups providing a full suite of creative data technology and media services to its global clients in 145 countries. We empower the worlds leading brands to create better creative and customer experiences with a focus on digital and creative transformation. We are champions for innovative marketing success across a diverse portfolio of fortune 100 companies, leading organisations and clients across key iconic brands advertising globally to maximise the value of our customer portfolios to drive hyperpersonalised marketing and creative strategies. Our combined strengths in creative excellence, customer digital transformation, media and enterprise marketing technology drives our competitive advantage. This includes a wide range of services to clients across media advertising, creative PR, television format production, sports stadium advertising, talent and influencer engagement, app and website development, game development, programmatic advertising, IT consultancy and data products analytics and insight. You can learn more about us here: role:

Role: Data and Analytics Strategy Manager is the lead to the agencys data strategy and analytics initiatives. The core responsibility is to develop and implement data-driven strategies to drive business growth and improve decision-making processes across the organization.

• Build strong relations and collaborate with operational, product, business performance, data science, and data visualization leads.
• Build analysis and correlation derived from different data sources (i.e. research, sales analytics, media metrics, etc.).
• Produce insights, derive analysis, and produce point of view on data output.
• Collaborate with business leads to build data and analytics solutions for agency accounts (through Google Analytics, Data Visualization, etc.).
• Reports into Performance director as function to drive performance and business intelligence for clients.
• Develop and execute the overall data and analytics strategy aligned with the companys goals and objectives.
• Establish and enforce data governance policies to ensure data quality, consistency, and security.
• Lead a team of data analysts and data scientists to deliver actionable insights and recommendations.
• Work closely with business leaders, IT and other stakeholders to understand data needs and deliver solutions that meet those needs.
• Design measurement frameworks based on different goals and KPIs to measure the success of data initiatives and their impact on the business.
• Evaluate and implement the latest data technologies and tools to enhance data collection, storage and analysis capabilities.
• Coordinate with data science team and operational excellence lead to develop and maintain dashboards and reports to provide regular updates on data analytics initiatives and business performance.
• Stay abreast of industry trends and best practices to continuously improve the companys data strategy and analytics processes through collaboration with tech and measurement partners like Google, Meta, Tableau, Salesforce, etc.

• AI & Machine Learning : Design and deployment of advanced analytics and ML models (e.g. fraud detection, recommender systems, segmentation).
• Marketing & Customer Analytics : Campaign measurement, segmentation, churn prediction, customer LTV, media mix modeling and ROI optimization.
• Causal Inference & Statistical Modeling : Specialized in impact analysis and marketing event evaluation.

• Team Leadership : Manages a team of data scientists and cross-functional teams including data engineers and BI analysts.
• Stakeholder Management : Extensive experience engaging with CXOs and business heads.
• Program & Project Management : Leads end-to-end data science delivery across industries and regions.

• Languages : Python, R, SQL, PySpark, Scala
• ML Frameworks : TensorFlow, Keras, XGBoost, SVM, GBM
• Platforms & Tools : AWS, GCP, Azure, Snowflake, Databricks, Tableau, SAS

• Retail, Finance, CPG, Automotive, Technology is highly preferable.

• Competitive financial package.
• Annual bonus (based on company profitability and employee performance).
• Annual economy return tickets to home country for self and family (spouse and children if applicable).

• Flexible hybrid model and working schedule.
• Private personal and family healthcare.
• 3 wellness days to boost your energy and recharge yourself.

• Highly diverse, multicultural & collaborative team spirit
• Regular team socials throughout the year
• Friendly, approachable office atmosphere and business-casual dress code

• Dentsu University internal online portal access to ongoing online training and development opportunities to enhance your skills and advance your career.
• Internal mobility opportunities and regional exposure across MENA

Dentsu International is an organisation committed to equity, inclusion and diversity to drive our business results and create a better future every day for our employees, global clients, partners and communities. We believe a diverse workforce allows us to match our growth ambitions and drive inclusion across the business. We are interested in every individual bringing their whole self to work and this includes you

Please apply online and your application will be reviewed against our requirements. If your profile matches our business requirements our recruiters will make sure to get in touch with you soon. Should you not meet our immediate requirements your profile will be registered in our talent pool system and we will match your profile to suitable future vacancies.

Dubai

Dentsu

Full time

Permanent

Required Experience:

Manager

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