Data Analyst - Python/SQL (2-3 yrs)

Role : Hybrid model(3 days from office)

Responsibilities :

- Data Analysis : Conduct in-depth analysis using advanced Python libraries (e.g., pandas, NumPy) and SQL.

- Statistical Analysis : Apply statistical methods (e.g., hypothesis testing, regression) to extract insights from data.

- Data Visualization : Create clear, informative dashboards and reports using tools like Tableau, Power BI, or Matplotlib/Seaborn.

- Business Insights : Translate complex data into actionable recommendations.

- Data Quality : Ensure data accuracy, completeness, and integrity across all sources.

- Data Modeling : Design and maintain predictive models to support business decision-making.

- Collaboration : Work closely with cross-functional teams, including product, marketing, and engineering, to support data-driven strategies.

Requirements :

- 2-3 years of experience in data analysis, preferably with a focus on statistical methods or machine learning.

- Strong proficiency in SQL and Python, particularly in libraries used for statistical analysis (e.g., statsmodels, SciPy).

- Familiarity with data visualization tools (e.g., Tableau, Power BI) and Python-based visualization libraries (e.g., Matplotlib, Seaborn).

- Strong understanding of data modeling concepts, including experience with databases and cloud-based platforms (e.g., AWS, GCP, Azure).

- Proficiency in statistical techniques, including hypothesis testing, A/B testing, regression analysis, and time series analysis.

- Knowledge of machine learning algorithms (e.g., random forests, k-means) is a plus.

- Excellent communication skills, with the ability to explain complex analysis to both technical and non-technical audiences.

Preferred Qualifications :

- Experience with big data platforms (e.g., Hadoop, Spark).

- Master's degree or higher in computer science, statistics, mathematics, or a related field.

- Experience implementing machine learning models in production environments.

- Familiarity with cloud-based data platforms and data engineering practices.