Data Science Lead

Job Description

Role: Data Science Engineer
Job Type: Remote
Experience: 9+ years
Tax Terms: 1099/W2 only
Work Authorization: USC, GC, H4-EAD, H1-B Transfer & GC-EAD
Duration: 12+ months

Key Responsibilities:

• Design, train, and test supervised and unsupervised machine learning models to solve complex business problems.
• Utilize Python to develop automation frameworks and scripts for data processing, mining, and cleansing.
• Work with Data Science platforms such as SageMaker, Machine Learning Studio, and H2O to build and deploy machine learning solutions.
• Develop Generative AI projects leveraging various large language models (LLMs) to enhance product offerings and services.
• Leverage AWS services, including S3, Athena, EMR, Glue, Redshift, and Kinesis, for efficient data storage and processing.
• Automate ETL processes using Python and AWS infrastructure to ensure seamless data integration and transformation.
• Strong proficiency in SQL and Unix/Linux scripting to manage and manipulate large datasets.
• Collaborate with cross-functional teams to develop cloud and on-prem ETL solutions, using tools such as AWS Glue and Alteryx.
• Build and maintain CI/CD pipelines for data flow management using GitLab, ensuring efficient deployment and integration.
• Participate in DevOps/DataOps practices to streamline data workflows and enhance collaboration between teams.
• Utilize Kafka for data streaming and processing to support real-time analytics and decision-making.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
• 10+ years of experience in data science engineering or a similar role.
• 1-2 years of experience with Google Cloud platforms and services.
• Proficient in training and testing machine learning models, with a strong understanding of various algorithms and techniques.
• Extensive experience in Python, especially for data automation and processing.
• Familiarity with Data Science platforms (e.g., SageMaker, Machine Learning Studio, H2O) and their functionalities.
• Strong knowledge of AWS services and their applications in data engineering.
• Expertise in SQL and experience with Unix/Linux scripting.
• Experience with cloud/on-prem ETL tools and large-scale application development.
• Knowledge of CI/CD practices and tools, particularly for data flow management.
• Familiarity with DevOps/DataOps practices and tools, including Kafka for data streaming.

Preferred Skills:

• Experience developing Generative AI projects and understanding of large language models.
• Strong analytical and problem-solving skills with the ability to work independently.
• Excellent communication and collaboration skills to work effectively with cross-functional teams.