Juned is a Python Developer with 6+ years of experience in developing and deploying machine learning models and backend applications on cloud platforms. He has a strong background in data engineering and BI development, leveraging technologies like AWS and DataBricks.
Expert in Python for data preprocessing and ML model development.
Proficient in AWS services including S3 and Lambda for cloud solutions.
Strong experience with DataBricks for data engineering and real-time processing.
Skilled in CI/CD practices to ensure robust software delivery.
Demonstrated ownership in deploying machine learning models.
Successfully completed multiple ongoing data science projects, delivering actionable insights.
Streamlined data collection processes, enhancing BI development by 30%.
Developed robust data pipelines, improving data processing efficiency by 40%.
Deployed backend applications over scalable cloud infrastructure, ensuring high availability.
Built new KPIs to refine data results, supporting informed decision-making.
Overview: Developed a system to identify channel downtime for TV on-demand services before it impacts customer experience. Responsibilities: Utilized Python, ETL, DataBricks, and AWS services for data preprocessing and feature engineering. Integrated AWS Lambda and AWS Step Functions for serverless data pipelines. Leveraged Apache Superset for data visualization. Developed deep learning models using TensorFlow Keras for anomaly detection.
Key outcomes:
Developed a system to proactively detect channel outages, minimizing customer impact.
Created an anomaly detection model to identify unusual patterns in outage data.
Overview: Developed a predictive maintenance system for manufacturing equipment. Responsibilities: Utilized sensor data, maintenance logs, and historical failure data to predict equipment malfunctions. Used DataBricks for data preprocessing and feature engineering from diverse data sources. Stored large datasets in AWS S3. Developed ML models for predictive analytics.
Key outcomes:
Built a predictive maintenance system to forecast equipment failures.
Overview: Built a model to predict customer churn for a subscription-based service. Responsibilities: Performed data exploration and feature engineering using DataBricks. Managed data storage in AWS S3. Developed and trained ML algorithms for churn prediction.
Key outcomes:
Successfully developed a model to predict customer churn, enabling proactive retention strategies.
Overview: Developed a real-time sentiment analysis system for social media data. Responsibilities: Implemented DataBricks Streaming for real-time data processing. Stored processed data in AWS S3. Applied NLP techniques for sentiment analysis.
Key outcomes:
Created a real-time sentiment analysis system capable of processing social media data rapidly.
Overview: Built a fraud detection system for financial transactions. Responsibilities: Conducted data preprocessing and feature engineering using DataBricks. Managed data storage in AWS S3. Developed anomaly detection algorithms for fraud identification.
Key outcomes:
Developed a system capable of detecting fraudulent financial transactions.
Juned
Python+Azure