Sr.Data Scientist

If fashion is your passion, here is your opportunity to be working with the coolest digital ecommerce team in the Middle East, serving all your fashion needs in one place!

Ounass is your one stop destination for all things luxury making it The Definitive Home of Luxury. It’s our homegrown, local, e-commerce platform catering to the Middle Eastern customers across a realm of axes such as - Fashion consisting of Womenswear, Menswear & Kidswear, Beauty, Watches, Home & Jewelry.

At Ounass, we strongly believe that our culture is what brings us together and helps us succeed. We strive to be the leading luxury technology platform in the Middle East region, making sure we’re the first choice for our customers. We’re highly customer focus driven, ensuring that both our customers and employees are the core of the business. We’re recognized for our incredibly fast 2 hours delivery services within Dubai, while also providing a fabulous and dedicated Personal Shopping service for our VIP clientele.

We fall under the umbrella of a distinguished Al Tayer Group and are based in the ever happening and multi-cultural city of Dubai, United Arab Emirates. We currently are a growing family of 500 employees spread across the globe – UAE, United Kingdom, Turkey, Portugal, Ukraine and Sri Lanka. Ounass focuses and emphasizes on having a highly diverse and inclusive team consisting of 53 different nationalities, ensuring a strong cultural foothold. Our young, vibrant and growing team of 500 is extremely ambitious and is constantly innovating while growing the brand name and position together as a team.

The Senior Data Scientist plays a key role in the design, development, and advancement of data‑driven products, analytics, and machine learning solutions. The position works closely with cross‑functional teams to translate business challenges into scalable models and insights, while actively supporting team capability through knowledge sharing, coaching, and setting high standards in analytical thinking, technical quality, and communication. The role contributes to a strong data culture by encouraging collaboration, continuous improvement, and practical application of data to drive measurable business outcomes.

Responsibilities

Partner with product managers, engineers, and business stakeholders to understand objectives and translate them into analytical problem statements.

Contribute to defining opportunities for data science within strategic initiatives, and provide input to roadmap discussions without owning the roadmap.

Advocate for data‑driven decision‑making by communicating insights, constraints, and model results in clear, business‑focused terms.

Ensure that data science work is aligned with expected business outcomes and delivered with rigor, pragmatism, and speed.

Design a range of new Data Science Products, working with business stakeholders to identify key areas of potential growth for Ounass.

Functional Roles and Responsibilities

Partner closely with cross‑functional teams (Product, Planning & Merchandising, Growth, Engineering) to define problem statements, clarify business needs, and shape domain priorities.

Own the end‑to‑end development, maintenance, and improvement roadmap of the data science models or tools within your domain (e.g., OTB forecasting, demand prediction, customer lifecycle modeling).

Communicate model assumptions, performance, limitations, tradeoffs, and results in clear language tailored to non‑technical stakeholders.

Facilitate discussions that ensure alignment between DS outputs and business expectations.

Translate business requirements into clear technical plans, define milestones, and maintain a transparent backlog for your domain.

Ensure your domain’s models, tools, and analysis frameworks are robust, scalable, and aligned with expected business outcomes.

Identify opportunities for improvements or new features within your domain and propose iteration plans accordingly.

Lead end‑to‑end modeling workflows : data exploration, feature engineering, model development, evaluation, documentation, and deployment support.

Build predictive models using statistical, ML, and experimental methods to solve domain‑specific problems and deliver measurable impact.

Design and analyze A / B tests or other experiment frameworks to quantify model effectiveness and business uplift.

Work with engineering to integrate models into production environments, ensuring reliability, monitoring, and cost‑efficiency.

Improve existing methodologies by incorporating additional data sources, refining features, and optimizing model architectures.

Prioritize tasks within your domain based on business value, feasibility, urgency, and technical complexity.

Deliver high‑quality, well‑documented work with a strong bias for action while maintaining scientific rigor.

Ensure outputs (models, insights, analyses) are measurable, interpretable, and aligned to the KPIs defined with stakeholders.

Take ownership of solving problems fully – from initial exploration through delivery, validation, and iteration.

Develop and improve internal tools, scripts, or frameworks that accelerate experimentation, analysis, data preparation, or model monitoring.

Contribute to broader DS team practices : reproducibility, testing, documentation, and maintaining high standards of code quality.

People Management Roles and Responsibilities

Mentor junior data scientists and analysts by offering guidance on technical implementation, modeling strategy, and business framing.

Provide constructive feedback through code reviews, model reviews, and knowledge sharing.

Model strong communication habits and help elevate the team’s overall analytical clarity and rigor.

Participate in interviews and hiring assessments to help grow the team.

Knowledge and Skills

Experience using statistical computer languages (Python, SLQ, etc.) to manipulate data and draw insights from large data sets.

Minimum 2+ years of technology startup experience and in total 7+ years experiences.

Hands‑on experience developing, evaluating, and deploying machine learning models in production environments (preferably in e‑commerce or consumer‑facing products).

Experience with data architecture concepts, feature engineering, model monitoring, and debugging of data / model pipelines.

Familiarity with cloud platforms (AWS preferred), version control (Git), and production ML workflows.

Ability to build reliable, interpretable, and scalable models for forecasting, personalization, customer behavior, or optimization problems.

Knowledge and interest in utilizing current AI technology to optimize workflows and improve communication within and among teams.

Obsessed with finding the best way and getting things done at best quality and speed.

A drive to learn and master new technologies and techniques.

Experience interacting with cross‑functional stakeholders and aligning technical decisions with business constraints and priorities.

Strong ability to understand and combine business problems and data science approaches together to solve these problems.

Solid understanding of experimentation design (A / B testing, lift measurement, statistical significance, Bayesian or frequentist frameworks).

Ability to communicate tradeoffs, assumptions, limitations, and results clearly to non‑technical audiences.

Demonstrated skill in prioritizing workload based on business impact, urgency, and feasibility, and delivering results in a dynamic environment.

Excellent written and verbal communication skills tailored for both technical and business stakeholders.

What is in it for you!

The opportunity to shape the future of fashion with a dynamic & autonomous multinational team

Big potential for personal growth, training, and further development of skills

Attractive employee discounts across our brands

The latest technology tools

Non‑stop learning

365 days of loving what you do

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