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Data Scientist-Manager

PwC UK
Leeds
5 days ago
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PwC’s Data & AI Consulting team is rapidly expanding as we invest in building a new generation of Artificial Intelligence (AI) products that transform how we deliver value to our clients. We’re recognised by industry analysts, such as Gartner and IDC, as a market-leading Data & AI services consultancy and are actively working with clients to design, develop and deliver AI‑powered products and data capabilities that achieve tangible outcomes and business value.


About The Role

We’re looking for self‑starting, progressive, and inquisitive individuals who want to shape the future of how AI is applied in real business contexts. You’ll join a collaborative and entrepreneurial team that combines deep technical expertise with sector knowledge and product thinking.


Key Responsibilities

  • Work as part of a cross‑functional product squad—including AI Engineers, Product Designers, Data Scientists, and Industry Sector Specialists—to launch and scale AI client solutions, from core data science products (e.g., pricing and forecasting) all the way through to Agentic AI.
  • Design and advise on the data science approach for the product, balancing rigour, interpretability, and scalability, ensuring models are reusable across multiple client contexts.
  • Partner with sector and go‑to‑market teams and solution architects to identify client challenges, demonstrate product capabilities, gather feedback, and inform development priorities.
  • Collaborate closely with engineers to productionise models on cloud platforms (Azure, AWS, or GCP) using MLOps and DevSecOps practices.
  • Work with the Product Owner to monitor model performance and user feedback, continuously refining algorithms, enhancing feature design, and improving product outcomes over time.
  • Embed responsible and explainable AI principles into development to ensure outputs are trusted, transparent, and compliant with PwC’s standards.

Skills and Experience

  • Demonstrable practical project experience (professional or academic) in using applied analytics to solve business problems, including the data science lifecycle—from feature engineering and model design to validation, deployment, and monitoring.
  • Fluency in Python, SQL, or similar programming languages.
  • Experience using deep learning frameworks such as TensorFlow, Keras, PyTorch, or MXNet.
  • Familiarity with Agile and DevSecOps practices, including use of Git for version control.
  • Exposure to cloud environments (Azure, AWS, or GCP) and a desire to build solutions that scale.
  • Ability to explain complex data concepts clearly to technical and non‑technical audiences, with strong data storytelling and visualisation skills.
  • Intellectual curiosity with a disciplined, hypothesis‑led approach—validating, challenging, and refining outputs to ensure analytical rigour and business relevance.
  • Commercial curiosity and the desire to understand how analytics drives business outcomes.
  • A collaborative mindset—enjoy working in diverse, cross‑functional teams that involve onshore and offshore resources.

Seniority level

  • Entry level

Employment type

  • Full‑time

Job function

  • Engineering and Information Technology

Industries

  • Accounting

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