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Popular mid-level Data Scientist role with broad ML requirements, causing moderate competition.
Core ML/analytics skills transferable, though retail e-commerce experience preferred.
Explicit 4-8 years and mandatory ML, Python, SQL, Spark skills make shortlisting strict.
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Design and deliver ML and analytics solutions focused on forecasting, segmentation, churn, ranking, and personalization for retail and e-commerce.
Translate business needs into clear ML problems, perform deep exploratory data analysis, and communicate insights effectively to stakeholders.
Build scalable feature engineering and modeling pipelines (preferably Spark/PySpark), operationalize models with engineering teams, and ensure robustness via monitoring and retraining strategies.
4-8 years experience in data science, applied ML, or advanced analytics with proven production impact.
Bachelor’s or Master’s degree (or equivalent experience) in Data Science, Computer Science, Statistics, Mathematics, or related field.
Proficiency in Python (Pandas, NumPy, Scikit-learn) and solid SQL skills.
Experience working with large-scale data and Spark/PySpark; strong foundations in classical ML, feature engineering, and model evaluation.
Experience in retail or e-commerce domains including demand forecasting, pricing, inventory, or customer lifecycle analytics.
Familiarity with MLOps practices such as CI/CD basics, model monitoring, experiment tracking, and drift/retraining strategies.
Hands-on exposure to advanced AI techniques like Computer Vision and Generative AI/LLMs applied to retail use cases.