Key Responsibilities:

Data Exploration & Analysis: Utilize advanced Python libraries (such as pandas and NumPy) and SQL to perform thorough and insightful data analysis.

Statistical Techniques: Leverage statistical tools and methodologies-including regression, hypothesis testing, and other inferential techniques-to derive meaningful insights.

Visualization & Reporting: Design and build intuitive dashboards and visual reports using tools like Tableau, Power BI, or Python libraries such as Seaborn and Matplotlib.

Insight Generation: Transform complex datasets into clear, actionable business recommendations that inform strategic decisions.

Data Integrity: Monitor and maintain the accuracy, consistency, and reliability of data across multiple systems.

Predictive Modeling: Develop, refine, and support predictive models that aid in forecasting and data-driven decision-making.

Cross-Team Collaboration: Partner with teams across the business-including product, marketing, and engineering-to promote and support data-informed strategies.

Required Qualifications:

2-3 years of hands-on experience in data analysis, with an emphasis on statistical modeling or machine learning techniques.

Advanced skills in SQL and Python, particularly with statistical packages such as SciPy and statsmodels.

Experience using data visualization platforms (e.g., Power BI, Tableau) as well as Python tools for plotting and charting (e.g., Matplotlib, Seaborn).

Solid understanding of data modeling principles, and familiarity with working in environments that utilize cloud technologies like AWS, GCP, or Azure.

Strong background in statistical methods such as A/B testing, time series forecasting, regression analysis, and hypothesis testing.

Some exposure to machine learning methods like clustering (e.g., k-means) or ensemble methods (e.g., random forests) is advantageous.

Excellent communication skills, with the ability to convey technical analysis to both technical and non-technical stakeholders.

Preferred Qualifications:

Prior experience working with large-scale data platforms, such as Hadoop or Apache Spark.

A Master's degree or higher in a quantitative discipline such as statistics, mathematics, computer science, or related fields.

Experience deploying machine learning models into production environments.

Understanding of cloud-native data platforms and foundational data engineering workflows.