Data Analyst - ETL/Python (5-7 yrs)

Key Responsibilities :

- Collect, clean, and analyze large datasets from multiple sources to support business decisions.

- Develop and maintain dashboards, reports, and data visualizations to track key performance indicators (KPIs).

- Use statistical methods to identify trends, correlations, and anomalies in business data.

- Apply machine learning techniques (supervised, unsupervised, reinforcement learning) to solve business problems.

- Develop and maintain complex database queries and optimize database performance.

- Utilize Python libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch for analysis and development of complex ML/DL models .

- Utilize data visualization libraries such as Matplotlib and Seaborn for advanced visual representation.

- Develop and maintain efficient SQL queries and optimize database performance as a DB SQL Expert.

- Build and maintain ETL pipelines using Python (Pandas, PySpark, etc.) to streamline data workflows.

- Implement data engineering best practices for data processing, transformation, and storage.

- Work with data science and analytics tools such as Anaconda, Spyder, and Jupyter Notebook for development and experimentation.

- Present findings and recommendations to stakeholders through reports and presentations.

- Work with cloud-based AI/ML services (AWS, Azure, GCP) for model deployment and optimization.

- Work cross-functionally with engineering, product, and business teams to ensure data-driven decision-making.

- Leverage Natural Language Processing (NLP), computer vision, and other AI techniques where applicable.

Required Skills & Qualifications :

- Bachelor's or master's degree in data science, Computer Science, Statistics, Mathematics, or a related field.

- 5+ years of experience in data analytics or a related field

- Expertise in building and maintaining ETL pipelines using Python (Pandas, PySpark, etc.)

- Proficiency in SQL, Python (3.x and above) for data analysis.

- Experience with data science and analytics tools such as Anaconda, Spyder, and Jupyter Notebook.

- Experience with data visualization tools such as Tableau, Power BI, or Looker.

- Experience to develop ML/DL Models using Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch etc.

- Develop and fine-tune predictive models using Python, R, or other statistical tools.

- Work on feature engineering, model selection, and hyperparameter tuning to improve accuracy.

- Deploy, monitor, and retrain ML models as necessary to ensure continued performance.

- Experience working with relational and non-relational databases such as MySQL, PostgreSQL, MongoDB, or Snowflake.

- Expertise in data visualization using Matplotlib ,Seaborn , Google Data Studio etc.

- Excellent communication skills and ability to present data-driven insights to stakeholders.

- Strong experience in statistical analysis, hypothesis testing, and A/B testing.

- Experience with big data technologies (Spark, Hadoop) and cloud platforms (AWS,Azure,GCP).