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Your Key Responsibilities
You will be part of diverse teams of professionals across different geographies to deliver a wide range of data and analytics services. You will be solving complex issues and driving growth across financial services.
Skills and attributes for success
Fostering an innovative and inclusive team-oriented work environment
Working in diverse teams of professionals with different backgrounds
Demonstrating in-depth technical capabilities and professional knowledge
Contributing to thought leadership through white papers, point of views, and proof of concepts
Working in an entrepreneurial environment to pave your own career path
Ability to meet deadlines, own deliverables and contribute to project delivery
To qualify for the role, you must have
Undergraduate or master’s degree in a technical field like Computer Science, Econometrics, Mathematics, Engineering, or a related field
At least 2 to 5 years of related work experience in banking, capital markets, insurance, or asset management domain
Good communication (verbal and written) with the ability to effectively advocate technical solutions and results to technology and business audiences at all levels and disciplines within EY and client organizations
Experience working independently, efficiently, and effectively under extreme time constraints and delivering results by critical deadlines
Strong analytical and problem-solving skills
Experience with client-facing activities such as requirements gathering, facilitating meetings, presentation creation, and ability to prepare client ready deliverables
Proven track record of consistently meeting deadlines, and provide reporting and business recommendations in a dynamic environment
Good leadership and teaming skills
Strong organizational and time-management skills
Ability to integrate new knowledge and adapt to change with a natural curiosity and desire to learn
A willingness and ability to travel to meet client needs; travel is estimated at 60%
Valid Passport
A&I specific
Ability to understand business challenges and translate them into value-add analytics and insights solutions
Experience designing, building, and maintaining production-grade ML and DL models, machine learning workflows and pipelines
Demonstrated exploration of new techniques outside of day-to-day job duties
Helping our clients make data-driven decisions by working with structured and unstructured data sets and building out predictive models
Machine learning and data structure design, development, application of advanced analytical and statistical methods, and architecture experience
Building and applying data analysis algorithms (data mining, statistics, machine learning, natural language processing, RNNs, CNNs, etc.) as appropriate
Designing, architecting and developing solutions leveraging big data technology (Hadoop, Cassandra, Spark, Neo4j) to ingest, process and analyze large, disparate data sets
Ability to quantify improvement in business areas resulting from optimization techniques through use of business analytics and/or statistical modeling
Experience in applications of Neural Network architectures to Natural Language Processing, Computer Vision, Machine Intelligence and/or Reinforcement Learning
Collaborate with our data scientists to map data fields to hypotheses and curate, wrangle, and prepare data for use in their advanced analytical models
Understanding of the Python programming language and associated machine learning libraries/packages (sklearn, Tensorflow, PyTorch, statsmodels, etc.)
Experience in any of the tools/language/frameworks within the Big Data & cloud ecosystem (Hadoop, MongoDB, Neo4j, Spark, Hive, Hbase, Cassandra, etc.)
Proficiency in cloud-based AI services and machine learning platforms such as AWS SageMaker, Google AI Platform, Azure Machine Learning to build, train, and deploy scalable AI solutions.
Experience with cloud data warehouses like Snowflake, Google BigQuery, Amazon Redshift, and Azure Synapse Analytics for efficient data storage, integration, and analysis.
Familiarity with serverless architectures and cloud functions (AWS Lambda, Google Cloud Functions, Azure Functions) to create cost-effective, scalable machine learning inference endpoints.
Knowledge of containerization and orchestration technologies (Docker, Kubernetes) for deploying and managing AI applications in the cloud.
Expertise in generative AI models and techniques, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers, to create synthetic data, images, text, and other media.
Ability to leverage cloud-based GPUs and TPUs for training complex generative AI models efficiently and at scale.
Understanding of the ethical implications of generative AI and commitment to responsible AI practices, including transparency, fairness, and privacy considerations.
Ideally, you’ll also have
Master’s degree in Business Administration (MBA) or Science (MS) preferred
Prior consulting experience
EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets.
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