PwC
PwC’s Central Technology team (‘Tech Connect’) is responsible for identifying and deploying innovative AI use cases for PwC and its clients. We use machine learning and natural language processing to build data-driven solutions which solve important problems across our organisation.
We work across industries, including healthcare, financial services and professional services to develop their Machine Learning capabilities.
We’re currently looking for experienced Machine Learning Engineers with coaching and managing experience to join us across our Manchester, Leeds, Birmingham, and London offices.
What we do
Below are some examples of what machine learning engineers in the team have worked on before.
- Combine and scale natural language models that help subject matter experts (e.g. Risk, Auditing) to efficiently profile and analyse large sets of documents
- Applying advanced Privacy Enhancing Techniques to special category data to produce synthetic datasets that can be used by researchers/consultants without loss of privacy for individuals
- Scale out natural language processing models that identify climate change disclosures so that they can make billions of predictions across annual reports
The Role
- Designing, developing, and deploying production machine learning pipelines
- Contributing to our machine learning enabled, business facing applications
- Contributing effective, high quality code to our codebase
- Reviewing and monitoring live production models
- Leading and steering workstreams on projects for your specialism
- Coaching and managing junior engineers on workstreams
Core Skills
We make extensive use of the following technologies in our team.
- Python for machine learning development and deployment
- HTML, CSS, and Typescript for building our user facing front ends
- SQL for feature storage and manipulation
- Docker for packaging up our models for deployment
- Git for version control
- Azure / GCP for our cloud backend
Nice-to-haves
- Experience working with large data pipelines (using technologies such as Beam or Kafka)
- Exposure to other programming languages (such as Java)
- Experience of working on a project using agile concepts (such as working in sprints)
- Familiarity with working in an MLOps environment
- Experience working with search engines (such as Elasticsearch)
- Real world experience with deep learning
- Some experience in Privacy Enhancing Techniques (e.g. homomorphic encryption, federated learning, differential privacy, synthetic data generation with deep learning architectures)
Source ⇲
