Job Duties and Responsibilities:

• Analyze large-scale structured and semi-structured datasets to uncover patterns, trends, and insights that support business and product decisions.
• Develop, train, and evaluate machine learning models for use cases such as prediction, classification, anomaly detection, and forecasting.
• Perform exploratory data analysis (EDA) to understand data distributions, detect anomalies, and guide feature engineering strategies.
• Apply statistical techniques including hypothesis testing, regression analysis, and probability modeling to validate results and support decision-making.
• Design and implement feature engineering pipelines to transform raw data into meaningful inputs for machine learning models.
• Build and compare multiple models using appropriate evaluation metrics (accuracy, precision, recall, F1-score, ROC-AUC) and optimize performance through tuning.
• Work with large datasets using distributed computing frameworks or cloud-based platforms to ensure scalability and efficiency.
• Develop data visualizations, dashboards, and reports to effectively communicate analytical findings to technical and non-technical stakeholders.
• Collaborate with cross-functional teams including product managers, engineers, and business teams to translate business problems into data-driven solutions.
• Support the deployment of machine learning models into production by working with engineering teams and ensuring models meet performance and reliability standards.
• Monitor model performance over time and assist in updating models based on new data and changing business requirements.
• Write clean, modular, and maintainable code following best practices in software development and version control.
• Document analytical workflows, model assumptions, and results to ensure reproducibility and knowledge sharing across teams.

Technologies / Environment involved:

• Distributed storage: AWS Cloud Storage (S3), Google Cloud (GCP – Cloud Storage, BigQuery)
• Database management: MongoDB, SQL (Relational Databases)
• Machine learning: TensorFlow, PyTorch, Scikit-learn, NumPy, Pandas; exposure to SpaCy, NLTK, HuggingFace, Gensim, OpenCV
• Programming Languages:Python, SQL
• Data Visualization:Matplotlib, Seaborn, Plotly
• Development Tools: Jupyter Notebook / JupyterLab
• DevOps Tools:Git, Bitbucket