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Popular junior data scientist role in a metro market increases applicant density despite a lesser-known employer.
ML/GenAI skills are transferable across industries but require specific tooling experience, so moderate sensitivity.
Requires specific ML/GenAI, MLOps, and deep learning stack skills, making candidate filters stringent.
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Develop, train, and evaluate machine learning models for classification, regression, forecasting, and NLP use cases under senior guidance.
Support Generative AI solution development involving LLMs, prompt engineering, embeddings, and integration into enterprise workflows.
Assist in ModelOps lifecycle activities including packaging, deployment, monitoring, and collaboration with engineering teams for AI solution operations.
Up to 3 years of hands-on experience in data science, machine learning, or related fields including internships or academic projects.
Strong Python programming skills; familiarity with pandas, scikit-learn, PyTorch and/or TensorFlow required.
Working knowledge of ModelOps/MLOps tools and concepts including experiment tracking, model registries, CI/CD pipelines, and containerized model serving.
Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, or related discipline.
Experience or practical exposure to Generative AI technologies including LLM APIs, Prompt engineering, RAG architectures, LangChain, or LangGraph frameworks.
Comfortable working collaboratively with Data, ML, DevOps engineers and developers to integrate models into enterprise solutions primarily on Microsoft Azure.
Ability to clearly communicate technical findings and translate customer requirements into practical AI and data science solutions.