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Strong employer brand, popular entry-level data scientist title, and hybrid work increase applicant competition.
Core ML/NLP skills are broadly transferable across industries, so background sensitivity is low.
Requires formal degree and ML/NLP competencies but no explicit years, indicating moderate filtering.
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Contribute to development and deployment of AI-driven solutions including Generative AI and NLP applications under senior guidance.
Assist in building and testing machine learning models and data science pipelines, focusing on model performance, reliability, and responsible AI.
Collaborate with cross-functional teams and support various stages of data science lifecycle including data preparation, model evaluation, and deployment support.
Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or related field.
Basic understanding of machine learning concepts, including supervised and unsupervised learning techniques.
Familiarity with transformer models, large language models (LLMs), Generative AI technologies, and Retrieval Augmented Generation (RAG) concepts.
Proficiency with Python and basic experience with data science tools and cloud platforms (AWS or Azure exposure is a plus). Work Experience Required: Entry-level / Not explicitly mentioned
Has foundational knowledge of machine learning, NLP, and Generative AI with eagerness to grow under mentorship in a fast-paced environment.
Operates effectively in collaborative, guided roles supporting AI model development and deployment workflows.
Demonstrates interest in advanced AI concepts such as RAG pipelines, AI agent frameworks, and responsible AI practices.