Job Summary
Support the development and validation of predictive analytics and machine learning models. Conduct statistical and exploratory data analysis on large datasets. Develop data pipelines and visualizations to enable data-driven decision-making across teams.
Responsibilities
- Develop, prototype, and validate predictive analytics and machine learning models under senior data scientists' guidance to support business insights.
- Perform statistical analysis and exploratory data analysis on large structured and unstructured datasets to validate hypotheses and identify business opportunities.
- Create optimized SQL queries and Python programs to extract, clean, and feature-engineer data pipelines for advanced modeling and analysis.
- Translate complex model outcomes and statistical findings into clear data visualizations and self-service analytical tools to facilitate cross-functional decision-making.
- Design and automate data quality monitoring, alerting workflows, and metrics tracking to ensure accurate inputs for analytical models.
- Collaborate with product, operations, and engineering teams to define data science project requirements and standardize core performance metrics across regional markets.
Required competencies and certifications
- Degree in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative discipline.
- Strong foundation in statistics, probability, regression analysis, and basic machine learning concepts.
- Proficient in Python programming, including libraries such as Pandas, NumPy, and Scikit-learn.
- Skilled in writing optimized SQL queries for data extraction and preparation.
Preferred competencies and qualifications
- Familiarity with data engineering concepts, ETL workflows, and handling large-scale datasets.
- Experience with modern data visualization and BI platforms such as Power BI, Tableau, or Quick BI to effectively communicate data insights.