AI & GEN AI Data Scientist-Experienced Associate Save for Later Remove job
At PwC, our people in data and analytics focus on leveraging data to drive insights and make informed business decisions. They utilise advanced analytics techniques to help clients optimise their operations and achieve their strategic goals. In data analysis at PwC, you will focus on utilising advanced analytical techniques to extract insights from large datasets and drive data-driven decision-making. You will leverage skills in data manipulation, visualisation, and statistical modelling to support clients in solving complex business problems.
Driven by curiosity, you are a reliable, contributing member of a team. In our fast-paced environment, you are expected to adapt to working with a variety of clients and team members, each presenting varying challenges and scope. Every experience is an opportunity to learn and grow. You are expected to take ownership and consistently deliver quality work that drives value for our clients and success as a team. As you navigate through the Firm, you build a brand for yourself, opening doors to more opportunities.
Examples of the skills, knowledge, and experiences you need to lead and deliver value at this level include but are not limited to:
- Apply a learning mindset and take ownership for your own development.
- Appreciate diverse perspectives, needs, and feelings of others.
- Adopt habits to sustain high performance and develop your potential.
- Actively listen, ask questions to check understanding, and clearly express ideas.
- Seek, reflect, act on, and give feedback.
- Gather information from a range of sources to analyse facts and discern patterns.
- Commit to understanding how the business works and building commercial awareness.
- Learn and apply professional and technical standards (e.g. refer to specific PwC tax and audit guidance), uphold the Firm's code of conduct and independence requirements.
Minimum Degree Required
Bachelor's Degree
Minimum Year(s) of Experience
1 year(s)
Demonstrates thorough-level abilities and/or a proven record of success managing the identification and addressing of client needs:
Building of GenAI and AI solutions, including but not limited to analytical model development and implementation, prompt engineering, general all-purpose programming (e.g., Python), testing, communication of results, front end and back-end integration, and iterative development with clients
Documenting and analyzing business processes for AI and Generative AI opportunities, including gathering of requirements, creation of initial hypotheses, and development of GenAI and AI solution approach
Collaborating with client team to understand their business problem and select the appropriate analytical models and approaches for AI and GenAI use cases
Designing and solutioning AI/GenAI architectures for clients, specifically for plugin-based solutions (i.e., ChatClient application with plugins) and custom AI/GenAI application builds
Processing unstructured and structured data to be consumed as context for LLMs, including but not limited to embedding of large text corpus, generative development of SQL queries, building connectors to structured databases
Support management of daily operations of a global data and analytics team on client engagements, review developed models, provide feedback and assist in analysis;
Directing data engineers and other data scientists to deliver efficient solutions to meet client requirements;
Leading and contributing to development of proof of concepts, pilots, and production use cases for clients while working in cross-functional teams;
Structuring, write, communicate and facilitate client presentations; and,
Directing associates through coaching, providing feedback, and guiding work performance.
Demonstrates thorough abilities and/or a proven record of success learning and performing in functional and technical capacities, including the following areas:
Managing AI/GenAI application development teams including back-end and front-end integrations
Using Python (e.g., Pandas, NLTK, Scikit-learn, Keras etc.), common LLM development frameworks (e.g., Langchain, Semantic Kernel), Relational storage (SQL), Non-relational storage (NoSQL);
Experience in analytical techniques such as Machine Learning, Deep Learning and Optimization
Vectorization and embedding, prompt engineering, RAG (retrieval, augmented, generation) workflow dev
Understanding or hands on experience with Azure, AWS, and / or Google Cloud platforms
Experience with Git Version Control, Unit/Integration/End-to-End Testing, CI/CD, release management, etc.
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