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Popular AI Engineer title, mid-level candidate pool, metro hybrid role, and strong employer brand increase competition.
AI/ML skills are transferable but Databricks, enterprise MLOps, and advanced degrees increase domain specificity.
Advanced degree, mandatory ML/LLM/MLOps and backend skills make screening technically strict.
Analyze and explore complex datasets using advanced statistical and data cleaning techniques to identify patterns and insights.
Develop, deploy, and monitor machine learning models including feature engineering, ensuring scalability and ongoing performance optimization.
Create visualizations and data-driven narratives to communicate findings to technical and non-technical stakeholders, influencing decision-making.
Master's or Ph.D. in Statistics, Mathematics, Computer Science, Data Science, or related quantitative discipline.
Demonstrated experience in data science, machine learning, and statistical modeling with proficiency in Python, Scala, or R.
Experience with machine learning frameworks (TensorFlow, PyTorch, scikit-learn), data manipulation libraries (Pandas, NumPy), SQL/NoSQL databases, and version control (Git).
Work Experience Required: Not explicitly mentioned in the JD.
Strong technical expertise in building and deploying ML models in production, including experience with large-scale data processing and machine learning algorithm application.
Experienced in working with Databricks platform, data visualization tools, and Large Language Models (LLMs).
Proficient in back-end development and RESTful API design, indicating capability to collaborate across data engineering and development teams.