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Generalist data scientist title, metro location, and broad technical requirements drive high applicant density.
Core Python, SQL, and modeling skills are highly transferable across industries, so low domain sensitivity.
Explicit 1–3 years requirement plus mandatory Python, SQL, and modeling skills create moderate filtering.
Translate ambiguous business problems into defined analytical and modeling tasks working with product and domain experts.
Perform exploratory data analysis on complex, noisy data to validate quality, uncover patterns, and generate hypotheses.
Build and productionize end-to-end data and modeling pipelines including feature engineering, model training, evaluation, and deployment with engineering collaboration.
Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, or related quantitative discipline.
1–3 years of experience in data science, data analysis, or ML engineering, or equivalent strong project/internship experience.
Strong proficiency in Python with libraries like pandas, NumPy, scikit-learn; working knowledge of SQL for data manipulation.
Experience with exploratory data analysis, statistical analysis, and structuring reusable code with software engineering best practices.
Comfortable working in interactive notebooks to iteratively explore data and build analytical intuition.
Experience collaborating with engineering teams to productionize models including packaging and versioning.
Background or familiarity with production ML systems, cloud platforms (AWS, GCP, Azure), or domains like supply chain and operations is a plus.