What Jobs are available for Big Data Solutions in Dubai?

A software development company is looking for a talented, long-term Lead DS Researcher.

We’re looking for a Lead Data Science Researcher who thrives in research-heavy environments and enjoys exploring uncharted territory with the support of a strong technical team.

This is a unique opportunity to drive forward new ideas and applications, not just optimize existing ones.

About the company

Company Top Remote Talent

The company is a team of experts providing analytical services to healthcare clients. You will join an international team of first class professionals who are passionate to create products that improve quality of medical services.

Responsibilities

You will lead a compact team of two data scientists, guiding them on high-impact research initiatives and experimental projects. Your role will involve pushing the boundaries of applied machine learning — especially in the context of medical and clinical data — and turning complex problems into innovative solutions.

Requirements

**What we’re looking for:**Exceptional analytical and statistical skills-comfortable with uncertainty, inference, and experimentation;

Strong background in different areas of ML (traditional classification and regression techniques, recommender systems, text data, clustering, etc.);

Solid experience with deep learning frameworks like PyTorch or TensorFlow;

Excellent Python skills (beyond Jupyter Notebooks) - ability to build clean, testable, production-ready code;

Familiarity with medical or life science data is a strong plus;

Expertise in SQL, Pandas, Scikit-learn, and modern data workflows;

Comfortable working in Google Cloud Platform (GCP) environments.

**Bonus points for experience with:**State-of-the-art NLP models, Transformers, Agentic Approaches for mixed (temporal and text) data analysis and summarization;

Experience with pipeline orchestration tools like Airflow, Argo, etc.;

Proven Experience with Anomaly Detection and Forecasting with explainability for temporal and mixed data;

Intermediate+ English — ability to participate in written discussions with international teams and clients.

Working conditions

**Benefits:**Join a mission-driven team working at the intersection of data, medicine, and impact;

Work on meaningful challenges with long-term value for public health and healthcare quality;

Collaborate with top-tier experts in a culture that values curiosity, autonomy, and innovation;

Fully remote-friendly setup with flexibility and trust at the core.

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We are seeking a Senior Data Science Engineer with expertise in building and operationalizing production-grade machine learning pipelines . The ideal candidate will have hands-on experience with MLFlow, Databricks, and Azure ML , and a strong background in developing ML solutions for healthcare predictive use cases . This role will be central to embedding ML models into enterprise data workflows and ensuring their ongoing performance.

• ML Pipeline Engineering : Design, build, and maintain scalable ML pipelines leveraging MLFlow, Databricks, and Azure ML.

• Model Development : Develop and optimize ML models, particularly for healthcare predictive analytics .

• Integration : Embed ML models into KPI-driven data processing and analytics workflows .

• Model Lifecycle Management : Implement model monitoring, drift detection, retraining, and continuous improvement strategies.

• Collaboration : Work closely with data engineers, data scientists, and business stakeholders to deploy solutions that deliver measurable impact.

• 6+ years of experience in machine learning engineering or applied data science .

• Hands-on expertise with MLFlow, Databricks, and Azure ML .

• Strong experience in developing and deploying predictive ML models , ideally within healthcare.

• Proficiency in Python, SQL, PySpark , and modern ML frameworks (TensorFlow, PyTorch, or Scikit-learn).

• Knowledge of model monitoring, drift detection, and automated retraining strategies .

• Familiarity with cloud-native ML architectures and CI/CD for ML (MLOps).

• Strong problem-solving and collaboration skills with a focus on production-grade delivery.

• Work on cutting-edge healthcare predictive analytics solutions .

• Gain exposure to enterprise-scale ML pipelines on modern platforms.

• Opportunity to drive end-to-end ML lifecycle ownership .

• Competitive compensation and career growth in a fast-growing data-driven organization.

Overview

MAIN OBJECTIVE OF ROLE To define and drive the enterprise strategy for extracting business value from data by leveraging advanced analytics, machine learning, and artificial intelligence to generate insights, develop predictive models, foster AI innovation, and integrate ethical and scalable AI into products, services, and decision-making processes that deliver competitive advantage.

• Leads the development and implementation of machine learning models, algorithms, and AI systems and explores innovative approaches to solving business problems using AI technologies.
• Drives continuous process improvement and optimization by staying informed about advancements in data engineering and AI, recommending new tools and technologies.
• Builds, mentors, and leads a team of data and machine learning engineers, and AI specialists by providing guidance, support, and professional development opportunities to team members.
• Develops and executes the organization’s data science and AI vision, ensuring alignment with business objectives and measurable outcomes.
• Identifies, designs, and implements AI and machine learning solutions that enhance products, services, and operational efficiency.
• Establishes frameworks for responsible, explainable, and bias-free AI use in compliance with regulations and ethical standards.
• Partners with product owners, business leaders, and data engineering teams to ensure AI initiatives are integrated, scalable, and business-driven.
• Monitors advances in AI research, tools, and technologies, proactively applying them to maintain competitive advantage.
• Defines KPIs and tracks ROI for AI initiatives to ensure value delivery and continuous improvement.
• Acts as a “data translator,” bridging technical and business domains to convert complex challenges into actionable AI-driven outcomes.

• Bachelor's Degree (3+ years)
• Degree in Information Technology, Computer Science, Data Science, or related field
• Fluent in English
• Deep understanding of data governance, compliance, and security best practices. Excellent understanding of machine learning concepts and algorithms. Proven track record of delivering AI solutions from concept to production at enterprise scale. Strong background in statistical modeling, machine learning, deep learning, and AI productization. Demonstrated success in translating business strategy into AI-driven initiatives with measurable ROI. Experience in regulated industries (e.g., Airline) preferred, with a strong understanding of ethical and compliant AI practices.
• Years with qualifications: 10 - 12 years
• Google Professional Machine Learning Engineer, AWS Certified Machine Learning – Specialty, Microsoft Certified: Azure AI Engineer Associate are an advantage

• Customer Focus
• Teamwork
• Effective Communication
• Personal Accountability & Commitment to achieve
• Resilience & Flexibility (Can do attitude)
• Decision Making
• Inspiring & Developing Others
• Strategic Thinking
• Business Acumen

Reads and complies with the ISR policies of the Company and diligently reports any weakness or incidents to the respective Line Manager or the Information Security team. Completes all required ISR awareness sessions and follows associated guidelines in the day-to-day business operations.

Data Science Manager - Customer | Al-Futtaim Automotive

Established in the 1930s as a trading business, Al-Futtaim Group today is one of the most diversified and progressive, privately held regional businesses headquartered in Dubai, United Arab Emirates. Structured into five operating divisions; automotive, financial services, real estate, retail and healthcare; employing more than 35,000 employees across more than 20 countries in the Middle East, Asia and Africa, Al-Futtaim Group partners with over 200 of the world's most admired and innovative brands. Al-Futtaim Group’s entrepreneurship and relentless customer focus enables the organization to continue to grow and expand; responding to the changing needs of our customers within the societies in which we operate.

By upholding our values of respect, excellence, collaboration and integrity; Al-Futtaim Group continues to enrich the lives and aspirations of our customers each and every day

Overview of the role
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.

What you will do

• 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.

Required skills to be successful

• 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.

About the team
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.

What equips you for the role

• 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.

About Al-Futtaim Automotive
A major division of the UAE-based Al-Futtaim Group of companies, Al-Futtaim Automotive is an industry leader with presence in 10 countries across the Middle East, Asia and Africa. Our core business activities at Al-Futtaim Automotive include distribution, manufacturing, leasing and aftersales, and we are firmly established as the regional representative of some of the world’s most iconic automotive brands: Toyota, Lexus, Honda, Jeep, Chrysler, Dodge, Volvo and RAM. We are driven by a customer-centric approach, constantly pushing the boundaries on innovation, quality standards, and value-added service across our vast universe of customers - right from motoring enthusiasts to fleet operators to contractors. Our mission is to become the leader in custom-made mobility solutions by delivering nothing less than world-class omni-channel experiences. We channel our local expertise and global trust to deliver one of the most comprehensive portfolios of mobility products and solutions, from passenger cars to SUVs, electric vehicles to high-performance motorbikes, commercial vehicles to industrial & construction equipment. What keeps the company moving forward is a 9000-member strong team, with inspiring possibilities for growth, throughout the career path. This is Al-Futtaim Automotive and we empower talent to move forward.

Data Science Manager - Finance | Al-Futtaim Automotive

Established in the 1930s as a trading business, Al-Futtaim Group today is one of the most diversified and progressive, privately held regional businesses headquartered in Dubai, United Arab Emirates. Structured into five operating divisions; automotive, financial services, real estate, retail and healthcare; employing more than 35,000 employees across more than 20 countries in the Middle East, Asia and Africa, Al-Futtaim Group partners with over 200 of the world's most admired and innovative brands. Al-Futtaim Group’s entrepreneurship and relentless customer focus enables the organization to continue to grow and expand; responding to the changing needs of our customers within the societies in which we operate.

By upholding our values of respect, excellence, collaboration and integrity; Al-Futtaim Group continues to enrich the lives and aspirations of our customers each and every day

As a Data Science Manager(Finance) at Al-Futtaim Automotive, you will lead AI and data science initiatives that optimize financial performance, strengthen risk management, and improve business decision-making. This role focuses on advanced predictive modelling, financial forecasting, and portfolio optimization to create measurable impact across the organization

• Build and deploy predictive models for residual value optimization, credit risk scoring, probability of default, and F&I leasing pricing optimization.
• Standardize financial KPIs and risk dashboards using statistical process control and advanced visualization tools.
• Generate financial insights through time series analysis, portfolio optimization, survival analysis, and risk modelling.
• Collaborate with finance and risk teams to identify and prioritize high-potential data-driven use cases.
• Lead and mentor a team of data scientists, ensuring the delivery of scalable machine learning and AI models

• Advanced expertise in time series forecasting, regression, logistic regression, and random forests.
• Strong skills in survival analysis, neural networks, reinforcement learning, and genetic algorithms.
• Deep knowledge of portfolio optimization, risk modelling, and causal inference techniques.
• Proficiency in deep learning (CNNs, RNNs, LSTMs, Transformers) and advanced pattern recognition.
• Strong understanding of financial regulations and their impact on AI/ML model development.
• Excellent stakeholder management and ability to translate complex financial models into actionable insights

You will report to the Head of Financial Data Science and lead a team of Data Scientists and a Data Science Manager. The team operates within the Finance Department, playing a critical role in enhancing financial planning, risk management, and profitability through advanced analytics

• Bachelor’s, Master’s, or PhD in Financial Analytics, Computer Science, or related field.
• 7+ years of experience in financial analytics, data science, and machine learning.
• Hands-on expertise in advanced statistics, optimization, causal and Bayesian modelling, time series forecasting, Monte Carlo simulations, graph neural networks, and recommender systems.
• Proficient in Python, SQL, Databricks, and ML libraries (TensorFlow, PyTorch, Scikit-learn, ARIMA/SARIMA, Prophet).
• Knowledge of NLP, autoencoders, transformers, NER, computer vision (OpenCV, YOLO), and model explainability (SHAP, LIME).
• Experience with MLOps, Git, and visualization tools (Matplotlib, Seaborn, Plotly).
• Strong leadership, problem-solving, and communication skills to manage teams and deliver business impact.

About Al-Futtaim Automotive : A major division of the UAE-based Al-Futtaim Group of companies, Al-Futtaim Automotive is an industry leader with presence in 10 countries across the Middle East, Asia and Africa. Our core business activities at Al-Futtaim Automotive include distribution, manufacturing, leasing and aftersales, and we are firmly established as the regional representative of some of the world’s most iconic automotive brands: Toyota, Lexus, Honda, Jeep, Chrysler, Dodge, Volvo and RAM. We are driven by a customer-centric approach, constantly pushing the boundaries on innovation, quality standards, and value-added service across our vast universe of customers - right from motoring enthusiasts to fleet operators to contractors. Our mission is to become the leader in custom-made mobility solutions by delivering nothing less than world-class omni-channel experiences. We channel our local expertise and global trust to deliver one of the most comprehensive portfolios of mobility products and solutions, from passenger cars to SUVs, electric vehicles to high-performance motorbikes, commercial vehicles to industrial & construction equipment. What keeps the company moving forward is a 9000-member strong team, with inspiring possibilities for growth, throughout the career path. This is Al-Futtaim Automotive and we empower talent to move forward.

Overview

Data Science Manager (Operations) at Al-Futtaim Automotive leads AI and advanced analytics initiatives that transform operational performance across the business. This role focuses on predictive modelling, anomaly detection, and process optimization to drive efficiency, reduce costs, and enhance customer satisfaction.

• Build and deploy predictive models for price elasticity, predictive maintenance, churn prediction, and anomaly detection in parts utilization.
• Standardize operational KPIs and real-time reporting systems across brands using statistical process control and advanced visualization techniques.
• Deliver actionable insights on sales and operational performance using causal inference, A/B testing, and counterfactual models.
• Collaborate with cross-functional stakeholders to identify new use cases, define business cases, and prioritize high-impact initiatives.
• Lead and mentor a team of data scientists, ensuring robust MLOps practices and scalable model deployment.

• Expertise in time series analysis, regression, and survival analysis for price elasticity and churn prediction.
• Proficiency in IoT data analysis, predictive maintenance algorithms, and unsupervised learning techniques for anomaly detection.
• Strong knowledge of causal inference, A/B testing, counterfactual modelling, and optimization algorithms.
• Familiarity with deep learning frameworks and pattern recognition for complex operational data.
• Strong stakeholder engagement and the ability to turn complex data insights into operational improvements.

You will report to the Head of AI and Data Science for Operational Excellence and lead a team of Data Scientists and a Data Science Manager. The team works within the Finance Department, playing a key role in improving efficiency, reducing risk, and unlocking value across all operational functions.

• Bachelor’s, Master’s, or PhD in Computer Science, Statistics, or a related field.
• 7+ years of experience in data science, operations analytics, or AI-driven business transformation.
• Hands-on expertise with advanced statistics, optimization, survival analysis, causal and Bayesian modelling, time series forecasting, graph neural networks, and recommender systems.
• Proficient in Python, SQL, Databricks, and ML libraries (TensorFlow, PyTorch, Scikit-learn, ARIMA/SARIMA, Prophet).
• Knowledge of NLP, transformers, autoencoders, NER, computer vision (OpenCV, YOLO), and model explainability (SHAP, LIME).
• Experience with MLOps, Git, and visualization tools (Matplotlib, Seaborn, Plotly).
• Strong leadership, communication, and project management skills.

A major division of the UAE-based Al-Futtaim Group of companies, Al-Futtaim Automotive is an industry leader with presence in 10 countries across the Middle East, Asia and Africa. Our core business activities include distribution, manufacturing, leasing and aftersales, and we are the regional representative of brands such as Toyota, Lexus, Honda, Jeep, Chrysler, Dodge, Volvo and RAM. We are driven by a customer-centric approach, pushing innovation, quality standards, and value-added service across our customer base. Our mission is to become the leader in mobility solutions by delivering world-class omni-channel experiences. We employ a 9,000-member team with growth opportunities across the career path. This is Al-Futtaim Automotive and we empower talent to move forward.

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.