Data Science Engineer, Engineer 2-2

1 month ago


Hyderabad, India Zelis Full time

Responsibilities - 
-----------------------

- Data Engineering: Work with data engineering teams to ensure data quality, availability, and reliability. Design and develop data pipelines for efficient data ingestion, transformation, and storage. Have knowledge of data partitioning, data shuffling, and parallel processing / async processing etc. 
- Big Data Processing (Spark): Utilize Apache Spark for large-scale data processing and analysis. Develop Spark applications to extract & transform massive datasets. Carry knowledge of data formats such as Parquet, ORC, Avro, and JSON and data compression techniques and tools, including gzip, Snappy, and zlib, to optimize storage and processing efficiency
- Data Analysis: Monitor and analyze data & pipelines to keep them top notch and effective. 
- Model Deployment: Deploy machine learning models into production environments, ensuring scalability, reliability, and performance. Monitor and maintain deployed models, implementing necessary updates and improvements.
- Machine Learning Engineering: Design, build, and optimize machine learning pipelines and workflows. Collaborate with data scientists tested ML models into production-ready models. 
- Performance Optimization: Continuously optimize algorithms, models, and data processing workflows for improved performance and efficiency. 
- Documentation and Reporting: Maintain clear and concise documentation of models, pipelines, and processes. Share the same with technical and non-technical stakeholders.


 
 
Qualifications - 
 
Carries 5-8 years atleast 
Hands-on experience with data science & machine learning / big data processing
Experience with model deployment in PROD environments & tools for MLOps (e.g., Docker, Kubernetes, CI/CD).
Expertise with big data processing using Apache Spark (includes processing structured / unstructured data processing & transformation).
Strong programming skills in Python, SQL (applies to all of the above work).
Experience with building robust & scalable data engineering pipelines / ETL processes.
Knowledge of cloud platforms (e.g., AWS, Azure, GCP) is desired.
Knowledge of Healthcare claims data is desired. 


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