oracle
Analyst-Data Science
American Express
Gurugram, IND
Full-time
Junior · 1+ years experience
Hybrid
Discovered 2 weeks ago
githivelangchainpythonsql
Key Skills for This Role
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Qualifications
- 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.
Responsibilities
- Execute analytical and data science solutions to solve business problems using statistical techniques, machine learning, and modern Generative AI approaches.
- Develop, test, and maintain analytical models and AI-enabled data products—including predictive models, prospect scoring, prioritization, matching, enrichment, and intelligent decision-support capabilities—to improve targeting and acquisition effectiveness.
- Design, build, and evaluate Generative AI workflows, including Retrieval-Augmented Generation (RAG), embedding-based retrieval, semantic search, prompt engineering, structured outputs, and LLM-powered applications, selecting appropriate approaches based on business requirements, solution quality, scalability, latency, cost, and governance considerations.
- Design and execute experiments to evaluate prompting strategies, retrieval configurations, model selection, and workflow architectures, continuously optimizing solution quality, response accuracy, operational efficiency, and business impact.
- Perform data extraction, preparation, feature engineering, and data quality validation using large-scale datasets to support AI/ML model development and analytical initiatives.
- Apply statistical and machine learning techniques to improve model performance through experimentation, feature refinement, validation, and continuous model optimization using established best practices.
- Collaborate with product, engineering, and business stakeholders to support the implementation, deployment, monitoring, and continuous improvement of analytical and Generative AI solutions within business workflows.
- Evaluate AI and GenAI solution performance using quantitative and qualitative evaluation techniques, including model performance metrics, retrieval quality, response accuracy, prompt robustness, business outcome measures, and production monitoring.
- Communicate analytical findings, model outputs, and business insights clearly through presentations and technical documentation for both technical and non-technical stakeholders.
- Ensure compliance with Responsible AI principles, model governance, data integrity, explainability, bias assessment, prompt safety, monitoring, audit readiness, and enterprise risk management standards throughout the analytical development lifecycle.
Preferred Qualifications
- Degree in a quantitative field preferred, such as Engineering, Mathematics, Computer Science, Finance, Economics, Statistics, or a related discipline.
- 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.
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