Senior Data Scientist

About Styli

Styli is a leading e-commerce marketplace launched in 2019 by Landmark Group, offering over 40,000 fashion and beauty styles to men, women, children, and beauty fans across the GCC and India. It specializes in trendy and affordable fashion with a vision to be the foremost aspirational value fast fashion and lifestyle destination, providing seamless service, personalized customer experiences across all engagement points, and continuously expanding its curated product range.

Role Overview

The Senior Data Scientist will independently manage high-impact data science projects end-to-end, from defining problems to deploying models and measuring their effect. This role involves collaboration with Product, Engineering, and business teams, while also providing mentorship and technical support to fellow Data Scientists.

Key Responsibilities

• Address complex business and product challenges through data analysis, statistical methods, and machine learning techniques.
• Lead projects across all phases: problem framing, data investigation, model development, experimentation, implementation, and impact assessment.
• Extract, clean, and analyze extensive datasets; create robust features, training datasets, and validation frameworks.
• Develop, assess, fine-tune, and enhance machine learning, statistical, or optimization models using relevant baselines and evaluation metrics.
• Perform thorough error analysis to identify biases, data leakages, quality issues, and ensure model robustness.
• Plan and interpret experiments or other evaluation techniques to quantify incremental business value.
• Collaborate with cross-functional teams to integrate data-driven solutions into production workflows.
• Monitor model performance, data integrity, operational functionality, and business outcomes after deployment.
• Balance technical trade-offs involving accuracy, interpretability, latency, scalability, maintainability, and cost-effectiveness.
• Produce maintainable, reusable, version-controlled, and well-documented code.
• Clearly communicate insights, recommendations, assumptions, and potential risks to both technical and non-technical stakeholders.
• Participate in technical reviews, code and model vetting, and provide guidance to other Data Scientists as necessary.

Required Qualifications and Experience

• Advanced skills in Python and SQL with a proven track record of developing maintainable, production-ready data science code.
• Substantial practical experience in building, evaluating, and deploying machine learning models, including model selection, experimentation, error diagnosis, and measuring business impact.
• Strong foundation in probability theory, statistical inference, experimental design, and awareness of bias, confounding factors, and data leakage.
• Demonstrated ability to independently manage multiple data science projects from conception to production and post-launch monitoring.
• Proficiency in designing reliable training and validation datasets and identifying data quality or instrumentation gaps upstream.
• Familiarity with production machine learning practices including batch/real-time predictions, APIs, continuous integration/deployment, model monitoring, and retraining procedures.
• Ability to make judicious technical trade-offs across model effectiveness, latency, scalability, maintainability, and infrastructure expenditure.
• Excellent stakeholder engagement and communication capabilities with a capacity to influence product and business strategies through data and measurable impacts.
• A proactive, results-driven approach, able to operate independently amid ambiguity and provide constructive technical leadership.

Preferred Skills and Knowledge

• Experience in areas such as recommendation engines, search and ranking algorithms, forecasting, pricing strategies, optimization, customer modeling, causal inference, computer vision, or generative AI.
• Knowledge of distributed processing, orchestration systems, backend APIs, cloud deployments, search or vector databases, caching mechanisms, and model deployment frameworks.
• Background working in e-commerce, retail, fashion, or other large-scale digital product sectors.

Educational Background

Possession of Bachelor's, Master's, or PhD degrees in Computer Science, Data Science, Statistics, Mathematics, Engineering, Economics, Operations Research or related quantitative fields is expected.

Experience Requirements

Typically, candidates have over 5 years of relevant data science or applied machine learning experience; equivalent expertise and tangible impact will also be considered.

Success Criteria

A successful Senior Data Scientist will effectively tackle complex problems independently, apply sound technical judgment, deliver dependable production solutions, influence cross-team decisions, and create measurable impacts for customers or business.