Data Scientist

Data Engineering & Scalable Pipelines
Design and development of scalable data pipelines using Python, SQL, PySpark, Apache Spark, Databricks, and Apache Airflow, processing large-scale IoT sensor data to support analytics, forecasting, and machine learning workloads.

Machine Learning & Predictive Modeling
Building and deploying machine learning models using Scikit-Learn, TensorFlow, XGBoost, and Random Forest for use cases such as customer churn prediction, fraud detection, and business forecasting.

Cloud & DevOps Technologies
Hands-on experience with cloud platforms including AWS, Azure, and Databricks, as well as containerization and version control tools such as Docker and Azure DevOps (Git) to support reproducible and scalable data workflows.

Big Data Technologies
Practical experience with Apache Spark, Databricks, and ETL processes to handle and transform large-scale datasets for operational analytics and business decision-making.

SQL & Database Management
Proficiency in SQL for querying relational and non-relational databases, supporting data warehousing and end-to-end data-driven solution development.

Data Visualization & Business Intelligence
Creation of dashboards and visual reports using Power BI and Tableau to communicate analytical insights and support business decision-making.

Optimization & Operations Research
Design of scalable optimization systems for shipment distribution, inventory simulation, and operational planning using data-driven algorithms, grounded in academic training in operations research and decision economics.

LLM & RAG Systems
Development of LLM-based knowledge assistants using retrieval-augmented generation (RAG) with semantic search and API integration for domain-specific question answering.

Programming Languages & Libraries
Strong programming skills in Python, SQL, PySpark, with use of libraries such as Pandas, NumPy, Scikit-Learn, and TensorFlow for data processing, modeling, and automation.