Analyst - Data Scientist

Achieving our goals starts with supporting yours. Grow your career, access top-tier health and wellness benefits, build lasting connections with your team and our customers, and travel the world using our extensive route network.

Come join us to create what’s next. Let’s define tomorrow, together.

Description

United's Digital Technology team is comprised of many talented individuals all working together with cutting-edge technology to build the best airline in the history of aviation. Our team designs, develops and maintains massively scaling technology solutions brought to life with innovative architectures, data analytics, and digital solutions.

Job Overview and Responsibilities

The Data Scientist designs, develops, and implements scalable AI and Generative AI solutions that improve customer experience, business efficiency, and decision-making. The role builds production-grade applications and workflows using Python, SQL, large language models, Retrieval-Augmented Generation, and agentic AI frameworks. It applies advanced analysis, prompt engineering, evaluation, and observability practices to deliver reliable and useful AI capabilities. The role partners with cross-functional teams to translate ambiguous business needs into practical solutions and clearly communicate findings and recommendations.

• Design, develop, test, and deploy robust AI and Generative AI solutions that improve customer experience, operational efficiency, and business decision-making.
• Build and deploy production-grade workflows, applications, and data pipelines using Python, SQL, relational databases, and modern AI tools.
• Perform advanced exploratory analysis and feature engineering across structured and unstructured datasets to support scalable AI and Generative AI use cases.
• Implement prompt engineering strategies, conduct LLM experiments, and apply output evaluation frameworks to ensure quality, relevance, accuracy, safety, and business usefulness.
• Develop and scale Retrieval-Augmented Generation pipelines and integrate agentic AI frameworks for multi-step reasoning and workflow automation.
• Implement AI observability and MLOps practices to monitor model behavior and system reliability; collaborate with cross-functional teams and communicate findings, risks, and recommendations to stakeholders.

This position is offered on local terms and conditions. Expatriate assignments and sponsorship for employment visas, even on a time-limited visa status, will not be awarded. This position is for United Airlines Business Services Pvt. Ltd - a wholly owned subsidiary of United Airlines Inc.

Qualifications

What’s needed to succeed (Minimum Qualifications):

• Candidates must hold a Master of Science or equivalent postgraduate degree in a STEM field.
• Data Science, Statistics, Engineering, Computer Science, Operations Research, or a related STEM field.
• 1–2 years of industry experience in AI engineering, data science, analytics, or machine learning, including practical experience moving models or analytical workflows toward production.
• Strong Python and SQL proficiency; software engineering fundamentals, including Git; experience with large relational datasets and platforms such as Microsoft SQL Server, Snowflake, BigQuery, or Teradata; practical knowledge of LLMs, prompt engineering, embeddings, RAG, and agentic AI; AI observability and MLOps; strong analytical problem-solving; ability to create clear Excel and PowerPoint presentations
• Exhibit strong spoken and written communication fluency
• Must be legally authorized to work in India for any employer without sponsorship

What will help you propel from the pack (Preferred Qualifications):

• Advanced coursework or specialization in machine learning, artificial intelligence, applied statistics, or cloud computing.
• Experience deploying Generative AI applications in a production or staging environment, particularly RAG or LLM-based workflows.
• Relevant cloud, machine learning, or MLOps certification
• Experience with RAGAS or TruLens; LangSmith or MLflow; regression, classification, ensemble methods, and common evaluation metrics; cloud platforms and cloud-based data environments such as AWS, Azure, or GCP.