Data Analyst – ETL/Python

• 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).

(ref:hirist.tech)