Degree in a quantitative field preferred, such as Engineering, Mathematics, Computer Science, Finance, Economics, Statistics, or a related discipline.
1+ years of experience in data science, advanced analytics, machine learning, decision science, or related quantitative roles.
Strong programming skills in Python and SQL, with working knowledge of Hive and/or PySpark in large-scale data environments, hands-on experience developing machine learning models end-to-end, and familiarity with software engineering best practices including Git-based version control.
Demonstrated hands-on experience building or experimenting with LLM-based applications, including Retrieval-Augmented Generation (RAG), prompt engineering, semantic search, embeddings, structured outputs, or AI-powered assistants.
Knowledge of supervised machine learning techniques (e.g., gradient boosting, tree-based models, regression, clustering) and statistical techniques such as hypothesis testing, multivariate testing, ANOVA, and model evaluation methodologies.
Exposure to AI agent frameworks, tool/function calling, vector databases, LLM orchestration frameworks (e.g., LangChain, LlamaIndex, DSPy), context management, or workflow automation is a plus.
Familiarity with modern ML/AI development frameworks, open-source libraries, prompt lifecycle management, and evaluation frameworks.
Strong analytical and problem-solving skills, with the ability to execute well-defined analytical tasks accurately and efficiently.
Demonstrated ability to manage assigned work independently while collaborating effectively within a cross-functional team.
High attention to detail, intellectual curiosity, and an experimentation mindset with the ability to evaluate solutions objectively and iterate based on evidence.
Strong written and verbal communication skills, with the ability to clearly explain analytical findings and support stakeholder discussions.
Familiarity with Responsible AI principles, including explainability, bias and fairness assessment, hallucination mitigation, prompt safety, model monitoring, evaluation, audit readiness, and GenAI risk management practices.
