Support the development, prototyping, and validation of predictive analytics and machine learning models under the guidance of senior data scientists.
Conduct statistical analysis and exploratory data analysis on large structured and unstructured datasets to validate hypotheses, identify patterns, and uncover business opportunities.
Develop optimized SQL queries and Python programs to extract, clean, feature-engineer, and prepare data pipelines for advanced modeling and analysis.
Translate complex model outcomes and statistical findings into intuitive data visualizations and self-service analytical tools to support cross-functional decision-making.
Assist in designing and automating data quality monitoring, alerting workflows, and metrics tracking to ensure high-fidelity inputs for analytical models.
Partner with product, operations, and engineering teams to define data science project requirements, standardizing core performance metrics across regional markets.
Requirements:
Degree in Data Science, Statistics, Computer Science, Mathematics, or a related highly quantitative discipline.
Strong foundation in statistics, probability, regression analysis and basic machine learning concepts.
Hands-on proficiency in Python (specifically libraries such as Pandas, NumPy, Scikit-learn) and SQL is required.
Familiarity with data engineering concepts, ETL workflows, and working with large-scale datasets is preferred.
Exposure to modern data visualization and BI platforms (e.g., Power BI, Tableau, Quick BI) to convey data insights effectively.
Support product, operations, and engineering teams in defining data science requirements and standardizing key business metrics across regional markets.
