Data Science Engineer

Data Science Engineer

Interview: Virtual/f2f 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.

Qualifications:

• 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.