Vanguard
MachineLearningEngineer,Specialist
Neural analysis suggests this role is
optimal for Mid candidates.
“Machine Learning Engineer, Specialist at Vanguard. Skills: Machine Learning, Data Engineering, ETL pipelines, Cloud platforms. Leverage data pipeline designs. Support development of data pipelines”
Industry & Context.
Root cause analysis
What They're Looking For.
Must Have
Undergraduate degree, 5 years of relevant work experience, 3 years of hands-on experience designing ETL pipelines using AWS services, Proficiency in programming languages, particularly Python, Familiarity with machine learning libraries and frameworks, Understanding of cloud technologies, including AWS and Azure, Experience with NoSQL databases, Solid understanding of software engineering principles, Knowledge of Machine Learning Development Lifecycle (MDLC) best practices, Understanding of solution architecture for building end-to-end machine learning data pipelines
Nice to Have
Graduate degree is preferred, Experience with API design and development is a plus
What You'll Do.
Leverage data pipeline designs
Support development of data pipelines
Support integration of model pipelines
Develop understanding of SDLC for model production
Review pipeline designs
Make data model design changes
Document design changes
Review design changes with data science teams
Support data discovery
Support automated ingestion for model development
Perform detailed analysis of raw data sources
Apply business context
Engage with internal stakeholders
Understand business processes
Probe business processes
Bring structure to requests
Translate requirements into an analytic approach
Participate in ongoing business planning
Participate in departmental prioritization activities
Run model monitoring scripts
Follow process for alerts to management
Address issues found in data pipelines
Participate in special projects
Perform other duties as assigned
How You'll Work.
Team & Collaboration
Data science teams; Internal stakeholders
Full Job Description
Supports and performs the development and programming of machine learning integrated software algorithms to structure, analyze, and leverage data in a production environment. **Core Responsibilities** * Leverages data pipeline designs and supports the development of data pipelines to support model development. Proficient with software tools that develop data pipelines in a distributed computing environment (PySprak, GlueETL). * Supports integration of model pipelines in a production environment. Develops understanding of SDLC for model production. * Reviews pipeline designs, makes data model design changes as needed. Documents and reviews design changes with data science teams. * Supports data discovery & automated ingestion for model development. Performs detailed analysis of raw data sources for data quality, applies business context, and model development needs. * Engages with internal stakeholders to understand and probe business processes in order to develop hypotheses. Brings structure to requests and translates requirements into an analytic approach. Participates in and influences ongoing business planning and departmental prioritization activities. * Runs model monitoring scripts, follows process for alerts to management as needed. Addresses issues found in data pipelines from model monitoring alerts. * Participates in special projects and performs other duties as assigned. **Qualifications** * Undergraduate degree or equivalent experience; a graduate degree is preferred. * Minimum of 5 years of relevant work experience. * At least 3 years of hands-on experience designing ETL pipelines using AWS services (e.g., Glue, SageMaker). * Proficiency in programming languages, particularly Python (including PySpark, PySQL) and familiarity with machine learning libraries and frameworks. * Strong understanding of cloud technologies, including AWS and Azure, and experience with NoSQL databases. * Familiarity with Feature Store usage, LLMs, GenAI, RAG, Prompt Engineering
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