Data Scientist | 6 to 9 years | Bengaluru

As a Python + Data Engineer + ML engineer you would be expected to have:

• Data Scraping: Develop robust data scraping solutions using Python to extract data from diverse sources, including websites, APIs, databases, and files in various formats (e.g., CSV, JSON, XML, PDF, HTML).
• Data Transformation: Cleanse, preprocess, and transform raw data into structured formats suitable for analysis and integration into data pipelines and systems.
• File Type Handling: Demonstrate expertise in handling different types of files and data structures, including structured databases and unstructured text or media files.
• at least 5 years of hands on experience writing effective, scalable code, develop back-end components using Python programming language
• experience in extraction, transformation, and loading of data from a wide variety of data sources using Python, SQL and Azure technologies;
• technical expertise with data models, data mining, and segmentation techniques
• great numerical and analytical skills, interpret trends and patterns
• build algorithms and prototypes, prepare data for prescriptive and predictive modelling
• familiarity with tools like Databricks, Spark, PySpark, Airflow, Spark-SQL, Hadoop etc.
• Design and develop machine learning models to solve business problems.
• Implement and optimize similarity-based models such as KNN and Cosine Similarity.
• Develop and deploy clustering models (e.g., K-Means, DBSCAN) and classification models (e.g., Logistic Regression, Random Forest).
• Preprocess and clean data to ensure high-quality inputs for models.
• Perform feature engineering to enhance model performance.
• Evaluate model performance using appropriate metrics and refine models as needed.
• Develop and maintain APIs for model serving and integration with other services.
• Implement CI/CD pipelines to automate the deployment and monitoring of machine learning models.
• Collaborate with cross-functional teams to understand business requirements and deliver data-driven solutions.
• Stay up-to-date with the latest advancements in machine learning and incorporate best practices.
• Document model development processes and maintain code repositories.