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Data Scientist - United Arab Emirates (UAE)

  • Senior
  • On-site in Abu Dhabi

Join the team driving a 5-year strategic Digital & AI Transformation for the UAE's largest company. As a Senior Data Scientist, you’ll lead client conversations and guide the Data Science team in developing models, algorithms, and insights aligned with strategic goals. You’ll oversee the full data lifecycle, collaborating with cross-functional teams to turn complex datasets into actionable solutions and measurable business impact.

ON SITE

100%

You need to work in Abu Dhabi at least until the end of the year 2026. If there is a fit, you can extend until August 2027.

After the project ends, you'll have the regular DKL conditions: working 40 hours per week, with the flexibility to organize your schedule as you see fit. Requirements include sufficient overlap with the teams you collaborate with and attendance at dailies and occasional client meetings. We know that personal wellness is essential for achieving results.

PROJECT

Perks
  • Accommodation in a five-star hotel, a €100 daily meal allowance, a corporate Uber account, and an expense card.
  • One monthly return flight to Spain (or a flight for a significant other to Abu Dhabi).
  • A €1,500 bonus, prorated based on the length of stay in Abu Dhabi.
  • Immediate repatriation in the event of geopolitical risk.

COMPENSATION

40-60K

Opportunities to grow and advance your career. Every year, you will have €500 explicitly allocated for your educational needs.

100€ Amazon Gift Card on Christmas.

Vacations: 23 days/year.

    The role

    Learn about your responsibilities, how you will work, and who you will work with.

    As a Senior Data Scientist, you'll lead conversations directly with client stakeholders and guide the Data Science team's work to build data models, develop algorithms, and surface insights that advance our clients' strategic goals across projects and internal initiatives.

    Your work will span the full lifecycle—gathering and analyzing large datasets, building predictive models, and delivering findings that drive actionable change. Partnering with cross-functional teams, you'll identify key data needs, streamline processes, and deliver solutions that power our clients' strategic plan.

    Responsibilities

    Your responsibilities will encompass a wide range of tasks, including but not limited to:

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    Leadership

    Lead conversations with key client-side subject matter experts, data engineers, analysts, and product managers to align data solutions with strategic goals.

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    Analysis

    Gather and analyze large datasets to identify trends and patterns that inform business strategy.

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    Modeling

    Build and validate predictive models (including regression, classification, and clustering) to address complex business challenges.

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    Engineering

    Design data pipelines and preprocessing workflows that ensure high-quality, accessible data.

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    Optimization

    Continuously improve model performance and scalability in production environments.

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    Innovation

    Research and apply the latest machine learning algorithms and tools.

    How will you work?

    You'll join the 15-strong McKinsey/DKL AI team, working on-site alongside data scientists, analysts, and software engineers to support DKL's data-driven goals. We stay aligned through daily check-ins and regular project meetings, held both in person and remotely, to keep communication open and everyone on the same page.

    Our tech stack centers on Python and its ecosystem for statistical analysis and machine learning, with data managed on a Kubernetes-based data lake and a suite of tools hosted on Azure.

    Who will you work with?

    image of Matías Pizarro Matías Pizarro Data Architect & Software Architect

    With 28 years in software development and 8 years as Head of Engineering at McKinsey & Company, Matias leads our technical vision. He specializes in data engineering, AI, DevOps, and team scaling, and has grown Power Solutions Tech from 2 to 200 developers in just 5 years. Matías keeps Python, Pandas, Django, FreeBSD, and Bash in his daily toolkit and is passionate about using the right tools for the job. His leadership inspires innovation and excellence across our technical teams.

    What makes you a fit?

    Your qualifications

    Requirements

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    Education

    Master's degree in Data Science, Statistics, Computer Science, or a related field.

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    Experience

    Strong experience building complex data science workflows in business contexts. Strong theoretical grounding in statistical learning.

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    Programming

    Proficiency in statistical programming (Python) and machine learning frameworks.

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    Processing

    Strong knowledge of data processing and transformation techniques (e.g., SQL, Polars, Pandas).

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    Modelling

    Proven experience building and validating machine learning models and algorithms.

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    Analytics

    Demonstrated ability to analyze large datasets and communicate insights effectively.

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    Cloud

    Familiarity with cloud platforms (Azure, AWS, or GCP) for data storage and model deployment.

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    Collaboration

    Excellent problem-solving skills and the ability to work independently in a client environment.

    Nice-To-Have

    • Experience with deep learning and neural network architectures.
    • Experience with linear optimization.
    • Experience with Airflow.
    • Experience leveraging LLMs across a variety of contexts.
    • Familiarity with Apache Spark / PySpark for data processing.
    • Knowledge of data visualization tools such as Tableau, Power BI, or D3.js.
    • Background in data science projects within a tech-focused or startup environment.

    What are the first 6 months like?

    Your first six months will be structured to support your learning, integration, and progression as you settle into your role. This period aligns with our review checkpoints at 1, 3, and 6 months, ensuring you have a clear pathway to success during your probation period.

    Month 1

    Your first month will focus on onboarding and getting grounded in our data platforms, engineering practices, and team workflows. You will have access to comprehensive technical documentation and training resources, meet key stakeholders across data, analytics, and product teams, and start familiarizing yourself with our data architecture, pipelines, and development tools. This phase is all about building a strong foundation—setting up your local environment, understanding our deployment processes, and reviewing active projects. At the end of the month, we will have a check-in to reflect on your experience, answer any technical or process-related questions, and ensure you have the support you need to move forward confidently.

    Months 2-3

    By month two, you will start taking on defined responsibilities within our data engineering projects, collaborating closely with your team to plan deliverables, estimate workloads, and coordinate progress across stakeholders. During this phase, you will begin owning smaller data pipelines or components within larger initiatives—whether that is building new data ingestion processes, optimizing existing workflows, or contributing to infrastructure improvements. This hands-on experience will help you build confidence with our tech stack and development practices. At the three-month mark, we will have a dedicated review to reflect on your progress, discuss any technical or operational challenges, and identify growth opportunities as you continue to deepen your impact on the team.

    Months 4-6

    With solid experience under your belt, by month four, you will be ready to lead your own data engineering projects more independently. During this stage, you will take ownership of end-to-end delivery—designing, building, testing, and deploying scalable data solutions that support our business needs. You will also focus on refining your technical skills, improving system performance, and contributing to best practices within the team. The six-month review will serve as a key milestone to evaluate your overall impact, technical growth, and collaboration while closing out the probation period and setting clear goals for your continued development within the team.

    What is the selection process?

    We aim to make our selection process smooth, informative, and enjoyable, ensuring it is a two-way street where we get to know each other.

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    Initial Meet & Greet

    A casual video call to introduce ourselves, discuss the role at a high level, and get to know each other's backgrounds and motivations. This call is designed to determine if we are a good mutual fit.

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    Role-Focused Interview

    A more focused discussion, diving into the role's specifics and exploring key data engineering scenarios you might encounter with us. This is where we will review some example cases, discuss your experience, and address any questions you may have about the day-to-day aspects

    03/

    Meet the Team Leads

    During this call, you will have the opportunity to meet some of our key team leads. This conversation helps you understand the company culture, our team dynamics, and the kind of cross-functional work you will be doing. It is also an opportunity to discuss the projects we are passionate about in more detail.

    04/

    Decision & Offer

    After the final discussion, we will circle back with a decision. If we are a good match, we will be excited to extend an offer and welcome you on board! If this is not the right fit, we will let you know and share our feedback, wishing you all the best on your career journey.

    Are you ready to take a new step in your career?

    Curious to find out more? Complete the formand send us your CV. And don't hesitate to ask questions!

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