Data Engineer - Scientist and MLOps

Overview

On Site
Depends on Experience
Full Time

Skills

API
Amazon Redshift
Amazon S3
Amazon SageMaker
Amazon Web Services
Analytical Skill
Apache Spark
Artificial Intelligence
Big Data
Data Analysis
Data Science
SQL
Python
Machine Learning (ML)
Machine Learning Operations (ML Ops)
Extract
Transform
Load

Job Details

Job Title: Data Engineer - Scientist and MLOps

Location: Seattle, WA

Type: Fulltime Position

Job Description:

Must Have Technical/Functional Skills

We are looking for a skilled AWS Data Scientist to join our team and contribute to building data-driven solutions that enhance decision-making, optimize operations, and deliver business insights. In this role, you will leverage AWS s advanced data and machine learning services to analyze large datasets, build predictive models, and deploy scalable machine learning solutions. The ideal candidate will have a solid background in statistical analysis, machine learning, and data science, along with hands-on experience with AWS tools for model deployment and data processing.

Roles & Responsibilities

  • Data Analysis and Exploration: Analyze large, complex datasets to extract meaningful insights and identify trends. Perform exploratory data analysis (EDA) using AWS data processing tools.
  • Model Development: Build, train, and evaluate machine learning models using AWS services such as SageMaker, and frameworks like TensorFlow.
  • ETL and Data Preparation: Work with AWS Glue, Redshift, Textract and other data engineering tools to preprocess, transform, and manage data for machine learning purposes.
  • Machine Learning Pipeline Development: Develop end-to-end machine learning pipelines on AWS to automate and operationalize the deployment of models at scale.
  • Collaboration: Work closely with data engineers, business analysts, and stakeholders to understand business needs and tailor data science solutions to meet those needs.
  • Model Deployment and Monitoring: Deploy models to production and set up monitoring systems to track performance, accuracy, and other key metrics. Use SageMaker and Lambda for model hosting and API development.
  • Documentation and Reporting: Document models, processes, and findings for stakeholders, enabling clear communication of results and decision support.

Technical Skills:

  • AWS Services: Hands-on experience with AWS SageMaker, Textract, Comprehend, Lambda, Glue, Redshift, and S3.
  • Machine Learning and Statistical Techniques: Strong knowledge of supervised and unsupervised learning techniques, time series analysis, and statistical modeling.
  • Programming: Proficiency in Python; SQL for data manipulation and transformation.
  • Data Wrangling: Experience with data preparation, feature engineering, and data transformation.
  • Big Data Tools: Familiarity with Spark, EMR, and other big data tools on AWS is a plus.
  • Problem Solving: Strong analytical and problem-solving skills, with a focus on translating business questions into data science problems.
  • Communication: Ability to communicate complex results clearly to both technical and non-technical stakeholders.
  • Certifications: AWS Certified Machine Learning - Specialty or AWS Certified Data Analytics - Specialty is a plus.

Preferred Qualifications:

  • Experience deploying deep learning models and knowledge of cloud-based ML/AI best practices.
  • Familiarity with MLOps and DevOps practices.
  • Experience with visualization tools such as QuickSight, Tableau, or similar.
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