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Lead Machine Learning Engineer
Posted on 3/10/2022
Deerfield, MA, USA • Chicago, IL, USA • New York, NY, USA • Portland, OR...
Experience Level
Desired Skills
Apache Spark
Data Analysis
Data Science
Data Structures & Algorithms
Microsoft Azure
  • Strong academic background in Machine Learning, Computer Science, Computer Engineering, Applied Statistics, or related field
  • Strong Python skills, familiar with SQL (any variant)
  • Hands on experience leveraging object orientated and / or functional programming
  • Hands-on experience with technologies such as Azure, Hadoop, Databricks, Spark, Spark SQL, ADF, Airflow and similar technologies
  • Experience building large-scale machine-learning infrastructure that have been successfully delivered to customers
  • Experience with approximate computing/statistical methods, working with massive data sets, and API development and MLOPS
  • 2+ years experiences developing cloud software services and an understanding of design for scalability, performance and reliability
  • 2+ years of experience in consulting or professional services
  • 2+ years of experience using advanced algorithms, programming languages, or technologies in the development of technical analytics solutions or capabilities
  • High level of independence, able to make time-sensitive decisions rapidly and solve urgent problems without escalation
  • Excellent communication skills, particularly on technical topics. Must be able to learn from others and teach others, and to work collaboratively as part of a highly interdependent team
  • Comfort with independent learning of new technologies, and willingness to jump into using unfamiliar tools
  • Other data science-adjacent technology experience would be beneficial but is not required, including: R, Docker, Rest APIs, Conda, Linux and basic shell scripting, C/C++
  • Strong time and project management skills; the ability to balance multiple simultaneous work items and prioritize as necessary
  • Developing backend/tools for to implement end to end MLOPs Patterns
  • Optimize Models for Performance, Accuracy, Software Eng best practices
  • Take models developed by data scientists and manage the infrastructure and operations around training and deploying those models
  • Build production systems to handle updating models, model versioning, and serving predictions to end users
  • Collaboration with the Development and Product teams to implement new algorithms as components of the Science Library and commercial products throughout the company
  • Optimize the interface between databases, both traditional and custom, and complex analytics that must run efficiently over vast quantities of data
  • Provide formal and informal guidance to data scientists and engineers within 84.51. Be responsive on Teams, Stack Overflow, and GitHub, and partner with stakeholders who need assistance with development practices within their teams. Help teams with technology migration efforts when engaged
  • Assist with internal technical education efforts, through occasional training sessions and the creation of documentation
  • Serve as early adopter of new technology, helping to build out best practices and providing feedback on the tooling to decision makers
  • Partner with a wide range of technical personas (i.e., engineering, architecture, data scientists) to identify and implement best practices around software engineering and analytic procedures
84.51 Degrees