Data Analyst

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 .

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 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)