Senior Data Scientist

We are looking for a Senior Data Scientist who can translate complex retail and supply chain problems into robust analytical and machine learning solutions. This role is critical in shaping our core AI capabilities—from problem framing and experimentation to model validation and business impact measurement. You will work closely with Engineers, Product, Analysts and Clients to ensure our models drive measurable value. Responsibilities: ● Lead end-to-end data science work by translating complex retail and supply chain business problems into well-defined analytical, statistical, machine learning, or optimization solutions, including demand forecasting, inventory optimization, replenishment, pricing, and promotion analytics. ● Perform deep exploratory data analysis, feature engineering, experimentation, and model development, selecting appropriate techniques and clearly addressing trade-offs between accuracy, process speed, scalability, and business constraints. ● Define, implement, and evaluate model performance using both technical and business-aligned metrics (e.g. WAPE, MAPE, VN1, bias, and service levels), including backtesting and validation against real-world scenarios. ● Partner closely with Engineers, Product, Analysts and Clients to ensure smooth transition from research to production, including defining data requirements, feature logic, validation rules, and post-deployment performance monitoring. ● Communicate insights, assumptions, limitations, and recommendations clearly to both technical and non-technical stakeholders, and contribute to data science best practices, reusable modeling frameworks, and mentorship of junior team members. Requirements: ● Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or related quantitative field. ● 4+ years of experience in data science, applied machine learning, or quantitative analytics, with a strong foundation in statistics, probability, time series analysis, and optimization techniques. ● Proven experience building and validating forecasting, predictive, or optimization models using real-world, large-scale, and imperfect datasets. ● Strong proficiency in Python and common datascience libraries (Pandas, NumPy, SciPy, Scikit-learn, Statsmodels); experience with PyTorch or TensorFlow is a plus. ● Solid experience working with SQL databases and collaborating with engineering teams to productionize models. ● Ability to design experiments, conduct rigorous backtesting, interpret results, and clearly communicate findings. ● Experience in retail, supply chain, or related domains is highly desirable; familiarity with cloud platforms, optimization solvers, or ML tooling is a plus.