EY is looking to solve the problem of helping clients navigate the complex world of modern data science, analytics, and software engineering by delivering the latest data science, big data technologies, and operations research techniques and practices.
Requirements
- 4 years of hands-on working experience with artificial intelligence, machine learning, data science, or data engineering.
- 4 years of experience working with Python.
- 2 years of combined experience using any combination of at least 2 of the following Generative AI models and frameworks: OpenAI family, Open source LLMs, Dall-e, LlamaIndex, Langchain, Retrieval Augmented Generation (RAG), Other frontier model families (e.g. Anthropic).
- 1 year of combined experience using DevOps tools GIT and Azure DevOps as well as Agile tools to develop and deploy analytical solutions with multiple features, pipelines, and releases.
- 2 years of experience designing, building, and maintaining ML models, frameworks, and pipelines as well as designing and deploying end to end ML workflows on at least one major cloud computing platform.
- 4 years of experience with data manipulation tools and libraries SQL, Pandas, or Spark.
- 2 years of experience with containerization and scaling models as well as integrating models and feedback from downstream consumption systems.
Responsibilities
- Work with a wide variety of clients to deliver the latest data science, big data technologies, and operations research techniques and practices to design, build and maintain scalable and robust solutions that unify, enrich and analyze data from multiple sources.
- Create well-rounded approaches to help clients navigate the complex world of modern data science, analytics, and software engineering.
- Provide clients with a unique business perspective on how data science and analytics can transform and improve their entire organization.
- Leverage Machine Learning (ML) workflows including ingesting, analyzing, and transforming data and evaluating results to make meaningful predictions and solve problems by incorporating data into decision making.
- Apply data mining and statistical analysis techniques like hypothesis testing, segmentation and modelling to analyze large amounts of data.
- Use generative AI modelling and frameworks and large language models (LLMs) to help clients make data-driven decisions by working with structured and unstructured data sets, building out predictive models and advising our clients on data mining leading practices.
- Build and apply data analysis algorithms including data mining, statistics, machine learning, natural language processing, sentiment analysis, and text mining.
Other
- Manage and motivate teams of professionals with diverse skills and backgrounds.
- Consistently deliver quality client services by monitoring progress.
- Demonstrate in-depth technical capabilities and professional knowledge.
- Maintain long-term client relationships and networks.
- Travel up to 80%, of which 10% may be international, to serve client needs.
