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Metro location, popular ML data scientist title, and known employer create moderate applicant density.
Requires deep learning, production ML deployment, and cloud skills so cross-industry transferable yet domain-specialized.
Many mandatory ML, deep-learning, cloud, and production-deployment skills but no explicit years makes filters moderately strict.
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Own end-to-end implementation and deployment of machine learning models, including data procurement, cleansing, feature engineering, model evaluation, runtime deployment, and monitoring.
Collaborate with product, sales, and engineering teams to shape AI-driven retail solutions and support sales enablement activities such as hardware estimation and client meetings.
Provide guidance to junior data scientists and participate actively in client engagements both online and onsite.
Bachelor’s Degree in Computer Science or related fields (Graduate degree preferred).
Proficiency in Python programming and solid understanding of data science and deep learning fundamentals.
Experience with ML/DL frameworks such as TensorFlow, PyTorch, Keras and knowledge of SQL and big data technologies like Snowflake, Apache Beam/Spark.
Work Experience Required: Not explicitly mentioned in the JD.
Practitioner with hands-on experience deploying scalable machine learning solutions in a cloud environment, preferably Google Cloud or Azure.
Experienced in collaborating cross-functionally with product, sales, and engineering teams in a complex technical environment.
Demonstrated ability to independently lead ML model lifecycle and mentor junior data scientists within a fast-paced and large-scale data processing context.