Role: AWS Data Science Engineer Location: Boston, MA — Hybrid, 4 days onsite About the Opportunity: We are seeking an experienced engineer with a passion for solving complex data problems and operationalizing machine learning at scale. In this position, you’ll design and maintain cloud-based data ecosystems, craft reliable pipelines, and develop models that deliver measurable business impact. This role blends software engineering, data architecture, and applied machine learning in a fast-paced, collaborative setting. What You’ll Do: • Create automated data workflows to collect, process, and integrate information from multiple sources using AWS Glue and related services. • Use Python and distributed processing tools like Apache Spark to prepare datasets, engineer features, and construct predictive algorithms. • Architect and optimize storage and retrieval strategies leveraging Snowflake and Amazon S3 for both structured and semi/unstructured data. • Apply AWS SageMaker to train, test, deploy, and track machine learning models in production. • Collaborate with analysts, product teams, and business leaders to translate requirements into scalable data science solutions. • Conduct exploratory analysis, generate visualizations, and communicate technical findings to non-technical audiences. • Implement best practices for security, governance, and quality control across the data lifecycle. • Automate MLOps workflows with GitHub Actions to enable streamlined CI/CD for model deployment. • Build and maintain Docker images to ensure consistent, portable deployments in various environments. Your Background: • Degree in Computer Science, Data Science, Statistics, or a related technical field. • 7+ years’ experience with AWS-based data engineering and analytics, including Glue, S3, SageMaker, and Snowflake. • Expert-level skills in Python for data processing, automation, and machine learning. • Proficiency with distributed computing platforms such as Spark for large-scale workloads. • Knowledge of supervised and unsupervised ML methods, performance evaluation, and tuning strategies. • Familiarity with SQL, NoSQL, and data warehousing best practices. • Experience delivering ML models into live environments and monitoring their accuracy and stability over time. • Understanding of MLOps concepts, version control for models, and deployment automation. • Hands-on experience creating CI/CD workflows using GitHub Actions and working with containerized environments via Docker. • Ability to translate technical concepts into actionable recommendations for stakeholders. • Certifications in AWS, Spark, or other relevant technologies are a plus.