Quantum World Technologies Inc. · 2 days ago
Machine Learning Engineer
Wonder how qualified you are to the job?
Maximize your interview chances
Insider Connection @Quantum World Technologies Inc.
Responsibilities
Work and collaborate with data science and engineering teams to deploy and scale models and algorithms.
Operationalize complex machine learning models into production including end to end deployment.
Understand standard Machine Learning algorithms (Regression, Classification) & Natural Language processing concepts (sentiment generation, topic modeling, TFIDF).
Working knowledge of standard ML packages like scikit learn, vader sentiment, pandas, pyspark.
Design, Develop and maintain adaptable data pipelines to maintain use case specific data.
Integrate ML use cases in business pipelines & work closely with upstream & downstream teams to ensure smooth handshake of information.
Develop & maintain pipelines to generate & publish model performance metrics that can be utilized by Model owners for Model Risk Oversight's model review cadence.
Support the operationalized models and develop runbooks for maintenance.
Qualification
Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.
Required
6+ years’ experience
Work and collaborate with data science and engineering teams to deploy and scale models and algorithms.
Operationalize complex machine learning models into production including end to end deployment.
Understand standard Machine Learning algorithms (Regression, Classification) & Natural Language processing concepts (sentiment generation, topic modeling, TFIDF).
Working knowledge of standard ML packages like scikit learn, vader sentiment, pandas, pyspark.
Design, Develop and maintain adaptable data pipelines to maintain use case specific data.
Integrate ML use cases in business pipelines & work closely with upstream & downstream teams to ensure smooth handshake of information.
Develop & maintain pipelines to generate & publish model performance metrics that can be utilized by Model owners for Model Risk Oversight's model review cadence.
Support the operationalized models and develop runbooks for maintenance.