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I will be data scientist for predictive modeling, machine learning, big data analytics
Senior Data Engineer and Data Scientist
Senior Data Engineer and Scientist with 15+ years of experience in Hadoop, Spark, PySpark,
SQL, data pipelines and Machine Learning. I specialize in building scalable big
data solutions, data warehous...
About this Gig
With 20+ years of experience across data science, data engineering, and business intelligence, I offer end-to-end data solutions from data collection and cleaning to machine learning modeling and actionable insights. Ive worked on advanced projects like flight delay prediction, customer retention modeling, and sentiment analysis using cutting-edge tools such as Python, Spark, Hadoop, and cloud platforms (Azure, AWS, GCP).
What I Offer:
- Predictive & Classification Models (Random Forest, XGBoost, SVM, etc.)
- NLP (Sentiment Analysis, Topic Modeling, BERT)
- Deep Learning (CNN, LSTM, ANN)
- Big Data Analysis using Hadoop, Spark (Scala, Java, PySpark)
- SQL & NoSQL Databases (SQL Server, Oracle, MongoDB, Cassandra)
- Data Visualization (Power BI, Tableau, Matplotlib, Seaborn)
- Data Pipeline & ETL (SSIS, Talend, Airflow)
- Cloud-based ML (Azure Databricks, AWS EMR, GCP BigQuery)
Tools & Languages:
Python, R, Java, Scala, SQL, PySpark, TensorFlow, Keras, Hadoop, Hive, Kafka, Power BI, Azure, AWS, GCP
Lets transform your raw data into valuable business intelligence. Whether it's a one-time analysis or a scalable ML pipeline, I ensure professional, explainable, and reliable results.
Programming language:
Python
•
SQL
•
Colab
•
Java
•
NoSQL
Technology:
Python
•
Java
•
Scala
•
TensorFlow
•
PyTorch
•
SQL
My Portfolio
FAQ
What information do you need from me to start?
To get started, I’ll need access to your data sources (or sample data), a brief description of your business goals, and any specific requirements like expected output format (e.g., dashboards, CSV reports, API endpoints). For large projects, a short discovery call is recommended.
Do you work with live/production data?
Yes, I have extensive experience building and managing production-grade data pipelines. However, I always recommend starting with a staging or sampled environment to validate the logic before moving to full production deployment.
