Data Science Engineer
2 days ago
Visa: USC, GC, GC EAD and H4 EAD
Onsite: Atlantic Ave, Boston, MA
JD:
We are seeking a talented Data Science Engineer to join our team and contribute to the development and implementation of advanced data solutions using technologies such as A WS Glue, Python, Spark, Snowflake Data Lake, S3, SageMaker, and machine learning (M/L).
Overview: As a Data Science Engineer, you will play a crucial role in designing, building, and optimizing data pipelines, machine learning models, and analytics solutions. You will work closely with cross-functional teams to extract actionable insights from data and drive business outcomes.
Key Responsibilities:
- Develop and maintain ETL pipelines using AWS Glue for data ingestion, transformation, and integration from various sources.
- Utilize Python and Spark for data preprocessing, feature engineering, and model development.
- Design and implement data lake architecture using Snowflake Data Lake, Snowflake data warehouse and S3 for scalable and efficient storage and processing of structured and unstructured data.
- Leverage SageMaker for model training, evaluation, deployment, and monitoring in production environments.
- Collaborate with data scientists, analysts, and business stakeholders to understand requirements, develop predictive models, and generate actionable insights.
- Conduct exploratory data analysis (EDA) and data visualization to communicate findings and trends effectively.
- Stay updated with advancements in machine learning algorithms, techniques, and best practices to enhance model performance and accuracy.
- Ensure data quality, integrity, and security throughout the data lifecycle by implementing robust data governance and compliance measures.
- Design and implement GitHub Actions workflows to automate MLOps pipelines, enabling continuous integration and continuous deployment (CI/CD) of machine learning workloads.
- Build and manage Docker images for containerized deployment of machine learning models, ensuring portability and scalability across environments.
- Bachelor's degree or higher in Computer Science, Data Science, Statistics, or related field.
- Proficiency in AWS services such as Glue, S3, SageMaker, and Snowflake Data Lake with 5-6 years of experience.
- Strong programming skills in Python for data manipulation, analysis, and modeling.
- Experience with distributed computing frameworks like Spark for big data processing.
- Knowledge of machine learning concepts, algorithms, and tools for regression, classification, clustering, and recommendation systems.
- Familiarity with data visualization tools with Tableau for creating meaningful visualizations.
- Excellent problem-solving, analytical thinking, and communication skills.
- Ability to work collaboratively in a team environment and manage multiple priorities effectively.
- Experience deploying machine-learning models in production environments and monitoring their performance.
- Knowledge of MLOps practices, model versioning, and automated model deployment pipelines.
- Familiarity with SQL, NoSQL databases, and data warehousing concepts.
- Strong understanding of cloud computing principles and architectures.
- Experience with GitHub Actions for automating CI/CD pipelines, particularly for machine learning workloads.
- Proficiency in building and managing Docker containers for deploying machine learning models in production environments.
- Certifications in AWS, Python, Spark, or related technologies.
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