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analytical data scientist

Delivery Hero

<div>Company Description 
Since launching in Kuwait in 2004, talabat has become the region&rsquo;s leading on-demand delivery app, serving millions of customers across eight countries. Our Quick Commerce (QC) Hub powers the grocery and convenience delivery experience &mdash; getting everyday essentials to customers&rsquo; doors in minutes, not hours.
Behind every delivery is data. Our QC data team works at a scale most data scientists only read about: millions of daily transactions, thousands of partners, and decisions that directly shape how people across the Middle East get their groceries delivered. We&rsquo;re building an analytics-first culture where every product and business decision is grounded in evidence. 
Job Description 
Why This Role
As a Data Scientist on the QC Hub team, you&rsquo;ll be the analytical brain behind one of talabat&rsquo;s fastest-growing verticals. You won&rsquo;t just crunch numbers &mdash; you&rsquo;ll partner directly with product and business leaders to shape strategy, design experiments that affect millions of users, and build the data foundations that power smarter decisions.
This is a role for someone who loves turning messy, ambiguous business questions into clean, actionable analysis &mdash; and who gets energy from seeing their insights change how a team operates.
What Success Looks Like<ul><li>
First 90 days: You&rsquo;ve ramped up on your domain, built relationships with your product and business partners, understood the data landscape, and delivered your first actionable analysis.</li><li>
By 6 months: You&rsquo;re the go-to analytical partner for your domain. You&rsquo;re independently designing and running experiments, and stakeholders regularly act on your recommendations.</li><li>
By 12 months: Your work has measurably improved decision quality in your domain. You&rsquo;ve built or refined data models that the team relies on daily, and you&rsquo;re mentoring newer team members on analytical best practices.</li></ul>
What You&rsquo;ll Actually Do
You&rsquo;ll spend roughly:<ul><li>
40% on deep analysis and experimentation &mdash; designing A/B tests, running multivariate experiments, doing deep dives into performance drivers, and turning findings into clear recommendations.</li><li>
30% on data modelling and quality &mdash; building and maintaining the data models that let us measure what matters, profiling source data, and ensuring data reliability.</li><li>
30% working with stakeholders &mdash; partnering with product and business managers to frame the right questions, set meaningful KPIs, and present insights that drive action.</li></ul>
Day-to-day, you&rsquo;ll:<ul><li>
Turn ambiguous business questions into structured analytical problems</li><li>
Build and maintain dimensional data models in BigQuery</li><li>
Design, execute, and interpret experiments (A/B and multivariate)</li><li>
Create automated dashboards and reports that stakeholders actually use</li><li>
Challenge assumptions with data &mdash; including your own</li><li>
Collaborate with data engineers on logging and data pipeline quality</li></ul>
You&rsquo;ll Thrive Here If You&hellip;<ul><li>
Love being embedded with business teams, not siloed in a data team</li><li>
Get satisfaction from changing how decisions are made, not just producing reports</li><li>
Are comfortable with ambiguity &mdash; many of your best projects will start as vague questions</li><li>
Care deeply about data quality and are willing to dig into source systems to understand what the data actually means</li><li>
Communicate clearly with non-technical stakeholders</li></ul>
This Might Not Be For You If You&hellip;<ul><li>
Want to build ML models full-time (this role is analytics and experimentation focused)</li><li>
Prefer working independently without regular stakeholder interaction</li><li>
Need clearly defined problems handed to you</li><li>
Are more interested in tools and techniques than business impact</li></ul> 

Qualifications 
What You Bring 
Education
Degree in a quantitative field (statistics, mathematics, economics, computer science, engineering, or similar) &mdash; or equivalent practical experience. A postgraduate degree is a plus but not required.
Must-Haves:<ul><li>
Strong SQL skills &mdash; you can write complex queries with window functions, CTEs, and optimise for performance on large datasets</li><li>
Reproducible analysis in Python or R &mdash; you write clean, well-structured analytical code, not one-off scripts</li><li>
Experiment design expertise &mdash; you understand when to use A/B vs. multivariate tests, can calculate sample sizes, and know the pitfalls of statistical testing</li><li>
Full analysis lifecycle experience &mdash; from problem framing through data auditing, analysis, interpretation, and presenting recommendations</li><li>
Data modelling knowledge &mdash; you understand dimensional design and can build models that serve both ad-hoc analysis and automated reporting</li><li>
Product analytics intuition &mdash; you&rsquo;re familiar with metrics like conversion, engagement, and retention, and know how to measure product health</li><li>
3+ years in data science, analytics, or a related quantitative field</li></ul>
Nice-to-Haves:<ul><li>
Experience with BigQuery and Google Cloud Platform</li><li>
Data engineering skills (Airflow, dbt, or similar pipeline tools)</li><li>
Experience with ML frameworks (Scikit-learn, XGBoost, LightGBM)</li><li>
Familiarity with modern data tools and AI-assisted analysis workflows</li><li>
Experience in an online consumer product or marketplace environment</li></ul> 

Additional Information 
What We Offer<ul><li>
Impact at scale &mdash; your work directly affects how millions of people get their daily essentials</li><li>
An analytics-first culture &mdash; a data team that&rsquo;s genuinely invested in analytical excellence, not just dashboarding</li><li>
Modern tooling &mdash; BigQuery, GCP ecosystem, and the freedom to experiment with new approaches including AI-assisted workflows</li><li>
Career growth &mdash; past data scientists on the team have grown into senior individual contributor roles and analytics leads</li><li>
Great Place to Work &mdash; talabat is a certified Great Place to Work across multiple countries in the region</li></ul></div>

Data Scientist II - Analysis, QC

Emirates

<div>
 
 Job Purpose
 
 
 
 A World leader in air cargo. We possess a geographical advantage that enables us to reach over 140 destinations across 6 continents. We are able to cater to two-thirds of the world&rsquo;s population in just 8 hours and serve major international trade regions quickly and efficiently. Our SkyCargo group works in partnership to facilitate global trade and connect customers with their destinations. We have a young, efficient, wide-bodied fleet, with ample capacity to transport cargo through our state-of-the-art, multi-airport hub in Dubai and adapt to our customers ever-changing needs.
 
 
 In the role of
 
 Data Scientist
 
 you will undertake Data Science (DS) projects through the application of mathematical modelling, machine learning, problem structuring and big data analysis to solve complex business problems across the Emirates Group portfolio with a focus on timely delivery and ownership of results.
 
 
 
 In This Role, You Will
 
 
 
 <ul>
 <li>
 Deliver analytics projects, applying appropriate statistical and machine learning techniques to produce key insights and actionable takeaways, and presenting findings and recommendations to the stakeholders.
 </li>
 <li>
 Develop dimensions of the DS project such as data analysis, visualisation and development of tools, models and documentation. Analyse business metrics and KPIs to understand trends, identify issues, and opportunities for optimisation.
 </li>
 <li>
 Address day-to-day technical issues, ensuring high quality of deliveries are achieved.
 </li>
 <li>
 Derive business insight from large, structured, semi-structured and unstructured, data sets and work with business stakeholders to deploy insights derived from this analysis to extract commercial value.
 </li>
 <li>
 Work with cross-functional team members, combining computer science and mathematics, to identify and inject high-value actionable insights into business processes at their point of highest impact.
 </li>
 <li>
 Work in quick iterations, using the techniques and algorithms best suited for solving the challenging problems through active involvement in the in the development of our analytics tools for A/B testing, segmentation, funnel analysis, and other analytics needs.
 </li>
 <li>
 Support sprint/work design and planning projects to bridge the gap between research and analytics by providing data-driven solutions across the required areas.
 
 
 </li>
 </ul>
 Qualification
 
 
 
 To be considered for this role, you must meet the below requirements:
 
 
 
 
 Qualifications:
 
 MSc in a relevant field such as data science, operations research, mathematics, science, data mining, artificial intelligence, applied statistics, machine learning or economics.
 
 
 
 Experience:
 
 5+ years experience in Information Systems.
 
 
 
 Knowledge &amp; Skills
 
 
 
 <ul>
 <li>
 Hands-on experience in Data Science ecosystem.
 </li>
 <li>
 Hands-on experience with big data technologies (Spark and Hadoop ecosystem) and deep learning frameworks (Tensorflow, Keras, etc.).
 </li>
 <li>
 Hands-on experience with SQL, R, Python.
 </li>
 <li>
 Hands-on experience with DevOps frameworks (Git, CI/CD, Docker).
 </li>
 <li>
 Hands-on experience building libraries and packages to be re-used by others.
 </li>
 <li>
 Hands-on experience building DS Solutions for production usage.
 </li>
 <li>
 Team player with a problem solving attitude.
 </li>
 <li>
 Experience of delivering advanced analytical projects to large, complex organisations in a multi-functional environment.
 </li>
 <li>
 Able to challenge and review the quality and value generated from analytical output.
 
 
 </li>
 </ul>
 At Emirates, we're committed to providing our employees with opportunities to grow and develop in their careers. So if you're looking for a challenging and rewarding opportunity, apply today and join our team!
 
 
 Salary &amp; benefits
 
 
 Join us in Dubai and enjoy an attractive tax-free salary and travel benefits that are exclusive to our industry, including discounts on flights and hotels stays around the world. Find out what it&rsquo;s like to live and work in our fast-paced, cosmopolitan home city in the Dubai Lifestyle section of our website www.emirates.com/careers
</div>

Data Scientist (Emiratisation)

talabat

<div>
 Since launching in Kuwait in 2004, talabat has become the region&rsquo;s leading on-demand delivery app, serving millions of customers across eight countries. Our Quick Commerce (QC) Hub powers the grocery and convenience delivery experience &mdash; getting everyday essentials to customers&rsquo; doors in minutes, not hours.
 
 
 Behind every delivery is data. Our QC data team works at a scale most data scientists only read about: millions of daily transactions, thousands of partners, and decisions that directly shape how people across the Middle East get their groceries delivered. We&rsquo;re building an analytics-first culture where every product and business decision is grounded in evidence.
 
 
 
 Job Description
 
 
 
 
 Why This Role
 
 
 
 As a Data Scientist on the QC Hub team, you&rsquo;ll be the analytical brain behind one of talabat&rsquo;s fastest-growing verticals. You won&rsquo;t just crunch numbers &mdash; you&rsquo;ll partner directly with product and business leaders to shape strategy, design experiments that affect millions of users, and build the data foundations that power smarter decisions.
 
 
 This is a role for someone who loves turning messy, ambiguous business questions into clean, actionable analysis &mdash; and who gets energy from seeing their insights change how a team operates.
 
 
 
 What Success Looks Like
 
 
 
 <ul>
 <li>
 First 90 days: You&rsquo;ve ramped up on your domain, built relationships with your product and business partners, understood the data landscape, and delivered your first actionable analysis.
 </li>
 <li>
 By 6 months: You&rsquo;re the go-to analytical partner for your domain. You&rsquo;re independently designing and running experiments, and stakeholders regularly act on your recommendations.
 </li>
 <li>
 By 12 months: Your work has measurably improved decision quality in your domain. You&rsquo;ve built or refined data models that the team relies on daily, and you&rsquo;re mentoring newer team members on analytical best practices.
 
 
 </li>
 </ul>
 
 What You&rsquo;ll Actually Do
 
 
 
 
 You&rsquo;ll spend roughly:
 
 
 
 <ul>
 <li>
 40% on deep analysis and experimentation &mdash; designing A/B tests, running multivariate experiments, doing deep dives into performance drivers, and turning findings into clear recommendations.
 </li>
 <li>
 30% on data modelling and quality &mdash; building and maintaining the data models that let us measure what matters, profiling source data, and ensuring data reliability.
 </li>
 <li>
 30% working with stakeholders &mdash; partnering with product and business managers to frame the right questions, set meaningful KPIs, and present insights that drive action.
 
 
 </li>
 </ul>
 
 Day-to-day, You&rsquo;ll
 
 
 
 <ul>
 <li>
 Turn ambiguous business questions into structured analytical problems
 </li>
 <li>
 Build and maintain dimensional data models in BigQuery
 </li>
 <li>
 Design, execute, and interpret experiments (A/B and multivariate)
 </li>
 <li>
 Create automated dashboards and reports that stakeholders actually use
 </li>
 <li>
 Challenge assumptions with data &mdash; including your own
 </li>
 <li>
 Collaborate with data engineers on logging and data pipeline quality
 
 
 </li>
 </ul>
 
 You&rsquo;ll Thrive Here If You&hellip;
 
 
 
 <ul>
 <li>
 Love being embedded with business teams, not siloed in a data team
 </li>
 <li>
 Get satisfaction from changing how decisions are made, not just producing reports
 </li>
 <li>
 Are comfortable with ambiguity &mdash; many of your best projects will start as vague questions
 </li>
 <li>
 Care deeply about data quality and are willing to dig into source systems to understand what the data actually means
 </li>
 <li>
 Communicate clearly with non-technical stakeholders
 
 
 </li>
 </ul>
 
 This Might Not Be For You If You&hellip;
 
 
 
 <ul>
 <li>
 Want to build ML models full-time (this role is analytics and experimentation focused)
 </li>
 <li>
 Prefer working independently without regular stakeholder interaction
 </li>
 <li>
 Need clearly defined problems handed to you
 </li>
 <li>
 Are more interested in tools and techniques than business impact
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 
 What You Bring
 
 
 
 
 Education
 
 
 
 Degree in a quantitative field (statistics, mathematics, economics, computer science, engineering, or similar) &mdash; or equivalent practical experience. A postgraduate degree is a plus but not required.
 
 
 
 Must-Haves
 
 
 
 <ul>
 <li>
 Strong SQL skills &mdash; you can write complex queries with window functions, CTEs, and optimise for performance on large datasets
 </li>
 <li>
 Reproducible analysis in Python or R &mdash; you write clean, well-structured analytical code, not one-off scripts
 </li>
 <li>
 Experiment design expertise &mdash; you understand when to use A/B vs. multivariate tests, can calculate sample sizes, and know the pitfalls of statistical testing
 </li>
 <li>
 Full analysis lifecycle experience &mdash; from problem framing through data auditing, analysis, interpretation, and presenting recommendations
 </li>
 <li>
 Data modelling knowledge &mdash; you understand dimensional design and can build models that serve both ad-hoc analysis and automated reporting
 </li>
 <li>
 Product analytics intuition &mdash; you&rsquo;re familiar with metrics like conversion, engagement, and retention, and know how to measure product health
 </li>
 <li>
 3+ years in data science, analytics, or a related quantitative field
 
 
 </li>
 </ul>
 
 Nice-to-Haves
 
 
 
 <ul>
 <li>
 Experience with BigQuery and Google Cloud Platform
 </li>
 <li>
 Data engineering skills (Airflow, dbt, or similar pipeline tools)
 </li>
 <li>
 Experience with ML frameworks (Scikit-learn, XGBoost, LightGBM)
 </li>
 <li>
 Familiarity with modern data tools and AI-assisted analysis workflows
 </li>
 <li>
 Experience in an online consumer product or marketplace environment
 
 
 </li>
 </ul>
 
 Additional Information
 
 
 
 
 What We Offer
 
 
 
 <ul>
 <li>
 Impact at scale &mdash; your work directly affects how millions of people get their daily essentials
 </li>
 <li>
 An analytics-first culture &mdash; a data team that&rsquo;s genuinely invested in analytical excellence, not just dashboarding
 </li>
 <li>
 Modern tooling &mdash; BigQuery, GCP ecosystem, and the freedom to experiment with new approaches including AI-assisted workflows
 </li>
 <li>
 Career growth &mdash; past data scientists on the team have grown into senior individual contributor roles and analytics leads
 </li>
 <li>
 Great Place to Work &mdash; talabat is a certified Great Place to Work across multiple countries in the region
 </li>
 </ul>
</div>

Huspy

<div>
 
 The Story So Far: We&rsquo;re Building a Global Brand in Real Estate
 
 
 
 Huspy is one of the leading property technology companies in EMEA.Launched in 2020, we now operate in multiple cities across the UAE and Spain, expanding into Saudi Arabia and 3 more European markets by 2026.
 
 
 Today, we own the largest portion of the UAE mortgage market and are one of the fastest-growing players in every European city we&rsquo;ve entered.We&rsquo;ve raised over $140 million (Series A and Series B) from the world&rsquo;s top investors, including Sequoia Capital, Founders Fund and Balderton Capital, to reshape the homebuying journey through powerful technology and agent-first tools.
 
 
 We&rsquo;ve built a SuperApp that empowers real estate agents and mortgage brokers, bringing cutting‑edge technology to one of the world&rsquo;s most traditional industries. We&rsquo;re transforming how property transactions happen &mdash; faster, smarter, and better for everyone.We&rsquo;re not slowing down.
 
 
 The question is: will you be part of what&rsquo;s next?
 
 
 
 The Main Event: What You&rsquo;ll Drive, Build, and Own
 
 
 
 <ul>
 <li>
 Real Estate Market Modeling: Build models applied to challenges such as valuation/pricing leveraging techniques from classic supervised ML to more advanced approaches.
 </li>
 <li>
 Multimodal Embeddings: Create vector representations of Real Estate entities, such as listings, combining images, text, and structured attributes to power search, matching, deduping, or recommendations.
 </li>
 <li>
 Data Analysis &amp; Experimentation: Use SQL/Python to extract, clean, and analyze data; design experiments and evaluate model-product impact with robust metrics.
 </li>
 <li>
 Model Operationalization: Ship models to production with capabilities such as monitoring, automated rollout, or CI/CD (in partnership with engineering).
 </li>
 <li>
 Cross-functional Delivery: Partner with product, engineering, and operations teams to translate business problems into scalable ML solutions.
 
 
 </li>
 </ul>
 
 The Perfect Match: What It Takes to Succeed at Huspy
 
 
 
 <ul>
 <li>
 Proven Experience: 4&ndash;8 years in applied data science/ML, delivering models that move real-world KPIs.
 </li>
 <li>
 SQL &amp; Python Mastery: Strong in frameworks such as Pandas/NumPy/Scikit-learn...building reliable data pipelines, model training and evaluation.
 </li>
 <li>
 MLOps Fundamentals: Experience deploying/maintaining models (batch or real-time), versioning, CI/CD basics, observability, and reproducible training.
 </li>
 <li>
 Communication &amp; Ownership: Clear with technical/non-technical stakeholders; can scope, prioritize, and explain tradeoffs.
 </li>
 <li>
 Comfortable with uncertainty, data quality issues, leakage risks, and market dynamics (location, seasonality, inventory shifts).
 </li>
 <li>
 Nice to Have: Software engineering experience; multimodal/vision experience; voice AI (ASR/NLU) exposure.
 </li>
 <li>
 Academic Background: Bachelor&rsquo;s in STEM (Master&rsquo;s a plus).
 
 
 </li>
 </ul>
 By submitting this application, I agree that my personal data will be collected, processed, and retained by the company solely for the purposes of managing and assessing my candidacy.
</div>

Data Scientist

COBBLESTONE

<div>Employment Type: Full-time &amp; Permanent

Location: Dubai, UAE (Visa sponsorship and relocation assistance provided)

Annual Base Salary: range of $80,000 - $110,000 per annum + performance-based bonus - 0% tax environment + opportunity to join our employee share scheme

Annual Leave: 30 Calendar Days

Public Holidays: As declared by UAE Government annually (8 to 14 days)

Working Days: Monday &ndash; Friday

Probation Period: 3 to 6 Months (As per UAE Labour Law)

Health Insurance: Comprehensive Private Health Insurance (IPD, OPD &amp; Dental)

Residency Visa Sponsorship: Provided by the Company

Relocation Support: Available for candidates relocating to Dubai

End of Service Gratuity: As per UAE Labour Law 

About Cobblestone Energy

Cobblestone Energy is an energy trading company specialising in the European Gas, Power and Emissions markets. Established in 2018, we combine deep analytics, advanced data science and agile technology to drive profitable trading decisions. Our flat organisational structure empowers every team member with autonomy and ownership. Our diverse global team of extraordinary talent is united by a relentless pursuit of excellence. 

Cobblestone Values
<ul>
<li>Excellence in Hiring &amp; Teamwork</li>
<li>Lifelong Learning Through Continuous Reflection</li>
<li>Independent Thinking Through a Meritocracy of Ideas</li>
<li>Winning</li></ul> 
 
Role Description:

We are seeking a highly motivated and talented Data Scientist to join our dynamic team. With a passion for data-driven solutions and a strong academic background, the successful candidate will play a critical role in shaping business decisions and driving operational excellence through advanced analytics, predictive modelling, and the strategic implementation of Generative AI/LLM technologies. 

What we offer in this role
<ul>
<li>A full-time, high-impact position within our dynamic Commercial team.</li>
<li>A team of exceptional colleagues, committed to continuous learning and pushing the boundaries of what's possible.</li>
<li>A career that challenges you to solve complex problems and rewards your drive for continuous innovation.</li>
<li>A unique opportunity to work on impactful projects, making independent decisions that drive real-world outcomes.</li>
<li>The chance to take full ownership of new projects, making high-impact decisions in a dynamic environment.</li>
<li>Equity participation, ensuring long-term alignment</li></ul> 
 
Key Responsibilities:
<ul>
<li>Analyse large, complex datasets to generate actionable insights that inform trading and commercial decisions.</li>
<li>Develop, deploy, and maintain predictive models to support market forecasting and trading strategy optimisation (e.g., time-series models, regression, tree-based methods).</li>
<li>Architect and implement production-ready AI/LLM solutions, such as Retrieval-Augmented Generation (RAG) pipelines or task-specific agents, integrated programmatically into analytical workflows (e.g., via APIs, LangChain, LlamaIndex).</li>
<li>Build, automate, and own end-to-end data workflows covering data ingestion, cleaning, feature generation, and model training, leveraging tools such as Python, SQL, Airflow, pandas, and workflow orchestration platforms (e.g., n8n or similar).</li>
<li>Apply advanced data science techniques, including synthetic data generation where appropriate, to improve model robustness, scalability, and operational efficiency.</li>
<li>Collaborate closely with traders and cross-functional stakeholders to translate business problems into high-quality, data-driven solutions.</li>
<li>Communicate insights and model outputs clearly and concisely, ensuring analytical work drives measurable trading and commercial impact.</li></ul> 
 
Requirements:
<ul>
<li>2-3 years of experience in data science or a related field, with a demonstrated record of achieving exceptional results.</li>
<li>Strong academic background in computer science, mathematics, statistics, or a related field</li>
<li>Outstanding problem-solving and critical thinking skills, capable of addressing complex, high-impact challenges.</li>
<li>Expertise in Python programming, with an ability to apply it to advanced data science problems.</li>
<li>Applied AI Proficiency: Demonstrated practical experience programmatically integrating LLMs (via APIs or frameworks like LangChain/LlamaIndex) into data pipelines or analytical applications.</li>
<li>Automation Focus: Proven ability to design and implement automated, end-to-end data workflows that leverage AI for efficiency gains.</li>
<li>Knowledge of machine learning algorithms and data visualization tools</li>
<li>Exceptional communication and collaboration skills, with a demonstrated ability to excel in fast-paced, cross-functional environments</li>
</ul>
This is an outstanding opportunity for a driven and talented Data Scientist to join an innovative trading company and make a significant impact on our business operations. If you possess a passion for data science and a proven track record of success, we encourage you to apply.

Note: Experience in the power trading domain is highly desirable but not essential. We value exceptional talent and the ability to learn quickly. 


Learn more about Cobblestone Energy careers: 
https://cobblestoneenergy.com/careers/

Why work with us: 
https://cobblestoneenergy.com/why-work-with-us/

Recruitment process: 
https://cobblestoneenergy.com/careers/vacancies/#process</div>

Data Scientist - Dubai, UAE

DICETEK LLC

<div><h1 class="jobSectionHeader">Role Purpose</h1>
The Senior Data Scientist will lead the development, optimization, and deployment of advanced card fraud detection models for issuing and acquiring businesses. This role involves end-to-end model lifecycle management, including training, hosting, evaluation, and deployment using SAS SFD and modern MLOps practices. The candidate will work closely with fraud risk, data engineering, and technology teams to ensure robust, scalable, and high-performing solutions.<h1 class="jobSectionHeader">
Key Responsibilities</h1><ul><li>Card Fraud Model Development &amp; Optimization: Design, develop, and optimize card fraud detection models for issuing and acquiring portfolios. Implement advanced statistical, machine learning, and AI techniques to improve fraud detection accuracy and precision and minimizing false positives.</li><li>Model Hosting &amp; Deployment: Deploy models using SAS SFD and integrate with production systems. Ensure seamless hosting and scalability of models across multiple environments.</li><li>MLOps &amp; Automation: Establish and maintain MLOps pipelines for continuous integration, deployment, and monitoring of fraud models. Automate model retraining and performance tracking processes.</li><li>Evaluation &amp; Testing: Conduct rigorous model validation, stress testing, and performance benchmarking. Collaborate with fraud operations teams to ensure models meet business and regulatory requirements.</li><li>Collaboration &amp; Stakeholder Management: Partner with fraud risk, data engineers, IT and business teams to deliver end-to-end solutions. Communicate insights and recommendations to senior management and business stakeholders.</li></ul><h1 class="jobSectionHeader"> 
Required Skills &amp; Qualifications</h1><ul><li>Education: Master&rsquo;s or Ph.D. in Data Science, Statistics, Computer Science, or related field.</li><li>Technical Skills: Strong proficiency in SAS (including SAS SFD), Python, and SQL. Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Hands-on experience with MLOps tools and practices (e.g., MLflow, Kubeflow, CI/CD pipelines). Deep understanding of card fraud detection techniques and transaction data.</li><li>Experience: 7+ years in data science roles, with at least 3 years in card fraud modeling. Proven track record of deploying models in production environments.</li><li>Soft Skills: Strong analytical and problem-solving skills. Excellent communication and stakeholder management abilities.</li></ul><h1 class="jobSectionHeader"> 
Preferred Qualifications</h1><ul><li>Experience in banking or payments industry.</li><li>Familiarity with cloud platforms (AWS, Azure, GCP) for model hosting and deployment.</li><li>Knowledge of regulatory compliance in fraud risk management.</li></ul></div>

Senior Fraud Data Scientist

Explore 500+ Analytical data scientist in Dubai this July. Find roles across top companies and apply now to boost your career.

500 analytical data scientist jobs In Dubai

Data Scientist II - Analysis, QC

Data Scientist (Emiratisation)

Data Scientist II - Analysis, QC

Data Scientist

Data Scientist - Dubai, UAE

Senior Fraud Data Scientist

Senior Fraud Data Scientist