Senior Data Scientist

Role overview

Key responsibilities

• Solve complex business and product problems using data, statistics, and machine learning.
• Own projects from framing and data exploration through modelling, experimentation, deployment, and impact measurement.
• Prepare large datasets and design features, training datasets, and evaluation frameworks.
• Build, evaluate, tune, and improve machine-learning, statistical, or optimization models.
• Analyze errors and identify bias, leakage, data-quality, and robustness issues.
• Design experiments to estimate incremental business impact.
• Integrate solutions into production and monitor model, data, operational, and business outcomes.
• Make technical trade-offs; write maintainable, version-controlled code; communicate findings and risks.
• Contribute to reviews and technical discussions and support other data scientists.

Must-have skills and experience

• Advanced Python and SQL proficiency and experience building maintainable, reusable, production-oriented data science code.
• Practical experience building, evaluating, and deploying ML models, including experimentation, error analysis, and impact measurement.
• Strong knowledge of probability, statistical inference, experimental design, bias, confounding, and data leakage.
• Independently owned multiple data science initiatives from framing through production and post-launch monitoring.
• Designed reliable training and evaluation datasets and identified upstream data-quality or instrumentation gaps.
• Working knowledge of production ML concepts, including batch or real-time inference, APIs, CI/CD, monitoring, and retraining.
• Can make trade-offs involving model quality, latency, scalability, maintainability, and infrastructure cost.
• Strong stakeholder-management and communication skills, with measurable customer or business impact.
• Proactive and outcome-oriented; able to work through ambiguity, take ownership, and provide technical guidance.

Good-to-have skills

• Experience in one or more of recommendation systems, search and ranking, forecasting, pricing, optimization, customer modelling, causal inference, computer vision, or generative AI is advantageous.
• Familiarity with distributed processing, orchestration, backend APIs, cloud deployment, search or vector databases, caching, or model-serving systems is advantageous.
• Experience in e-commerce, retail, fashion, or another large-scale digital product environment is advantageous.

Education and experience

• Bachelor's, Master's, or PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, Economics, Operations Research, or a related quantitative field.
• Typically 5+ years of relevant data science or applied machine-learning experience; equivalent depth, scope, and demonstrated impact are also considered.

Success profile