- Understand business objectives on location spoofing detection and develop models that help to achieve them, along with metrics to track their progress.- Explore and visualize data to gain an understanding of it, then identify differences in data distribution that could affect performance when deploying the model in the real world.- Define the preprocessing or feature engineering to be done on a given dataset, build robust feature engineering, and model inference pipeline in both batching and streaming modes.- Deploy models to production and build monitor dashboards to keep track of model performance over time.

- Select and integrate any Big Data tools and frameworks required to provide requested capabilities.- Implement ETL process to transform data from OLTP databases to OLAP DB and Data Lake using event streaming platforms such as Kafka.- Develop and transform large datasets and maintain robust data pipelines that can support various use cases with high performance.- Monitor performance and advise any necessary infrastructure changes.- Define data retention, data governance policies and framework

- Understand the business, knowledge, and insight.- Develop, transform large datasets and maintain robust data pipelines that can support various use cases with high performance. - Generate automated reports for customers with high-quality standards. - Develop and maintain interactive reporting tools. - Stay informed of database changes to ensure reports always work as expected.

- Build data pipelines to crawl data from the Internet, transform it, and ingest it into a Data Warehouse for analytics purposes.- Implement categorized services that separate data into multiple segments based on business requirements.- Manage the infrastructure, which includes Data tools like Data Warehouse, Airflow, Superset, etc., and keep the pipelines working smoothly from source to destination.- Build Machine Learning models that support data classification and extracting insight.