About the role
as a ml engineer, you will be responsible for identifying patterns and generating insights from application and event data using supervised and unsupervised ml approaches. These insights will be critical to accelerating go-to-market strategies and operational performance in customer-centric organizations. You will play a key role in the development, training, and performance management of ai models, analyzing diverse datasets from sources such as application telemetry, crm, support, accountingbilling, and web analytics. The position involves working closely with 1–5 customers as a highly trusted and billable ai/ml engineer. Joining an early-stage ai startup, you will also contribute to the growth of delivery methodologies and ai/ml workbenches.
responsibilities
* aggregate, analyze and generate supervised & unsupervised ml insights from large application telemetry data sets.
* consolidate and manage high-value datasets via rest apis or extract, load, transform (elt) processes, etc. into data lakes such as google big query, snowflake, databricks, aws s3, etc.
* build, train, test, tune and deploy ai models at scale within a customer’s operating architecture in platforms such as vertex ai.
* engage with other quad sci deployed colleagues on the explanation of data insights, confirmation of design requirements, root cause analysis, etc.
* work with customers on ai feature roadmaps (incl. Gen ai applications) for their models and ongoing performance management of deployed ai models.
* collaborate with quad sci colleagues and/or customer it on integration and automation requirements reliant on ai/ml score code outputs.
qualifications
* must have a bachelor’s degree in computer science, data science, statistics, mathematics or other related field.
* 3+ years of relevant experience in data science, consulting or product development.
* expertise in numpy, pandas, scikit-learn, keras, etc.
* demonstrated success in the use of clustering, classification, regression, decision trees etc to deliver transformative insights.
* strong experience in building, training, and tuning predictive machine learning, especially in the domains of next best offer or recommended action based on time-series type data sets.
* experience in natural language processing (nlp) for sentiment analysis and behavioral modeling.
* extensive experience in sql, python, pandas data transformation to form data tables and feature stores.
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