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Mid-level, popular Data Scientist title and 2-4 year range increase applicant density.
Core ML and MLOps skills transferable, though LLM/NLP focus adds moderate specialization.
Explicit 2–4 year requirement plus mandatory ML, deployment, and MLOps skills.
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Own end-to-end lifecycle of data science initiatives from requirement analysis to deployment and continuous improvement.
Design, develop, deploy, and monitor scalable machine learning models, analytical solutions, and automations that drive business performance and operational efficiency.
Develop reliable data pipelines, dashboards, and AI-driven solutions ensuring data quality, security, and adherence to governance standards.
Bachelor's or Master's degree in Data Science, Computer Science, AI, Statistics, Mathematics, or related quantitative field.
2–4 years of hands-on experience in data science, machine learning, predictive analytics, or AI.
Proficiency in Python, SQL, and data visualization tools, with experience developing and deploying ML models for business applications.
Experience with model deployment, MLOps, APIs, version control (Git), cloud platforms, and production monitoring.
Experienced in translating complex business requirements into scalable, production-grade AI and data-driven solutions with measurable impact.
Familiarity with emerging AI technologies including Generative AI, LLMs, NLP, prompt engineering, and RAG frameworks to innovate solution capabilities.
Comfortable working across cross-functional teams with strong ownership and continuous improvement mindset in AI and data projects.