As a Machine Learning Engineer you will use techniques such as machine learning and natural language processing to realise authentic, data-driven change and solutions. The team reports to the board and commercial executive and works with clients and PwC leadership across our business units to enhance performance and have impact on value creation.

Eligibility / Qualification Required:

Responsibilities:

• Designing and developing data science and machine learning assets for PwC and its clients
• Contributing effective, useful code to our Data Science codebase
• Participating in constant learning through training and skills development
• Deploying and managing machine learning models in production environments, ensuring scalability, reliability and performance monitoring
• Embedding Responsible AI practices across the model lifecycle, ensuring fairness, transparency, explainability, bias mitigation and compliance with ethical and regulatory standards
• Contributing to the strategy and growth of a fast developing data science capability
• Craft and communicate compelling business “stories” based on analytics insight
• Business case and Proposal development
• Presenting findings to senior internal and external stakeholders
• Being part of this technology innovation effort of the Firm

Key Skills Required:

• 4+ Years Experience
• Statistical Analysis & Machine Learning Theory – Excellent understanding of statistics, machine learning techniques and algorithms (Advanced):
  • Hands -on experience with regression, classification, clustering and other classical statistical models and algorithms
  • Independently formulate hypotheses, choose and justify appropriate statistical tests and interpret results
  • Select, implement and tune ML algorithms (e.g. random forests, SVMs, gradient boosting) end-to-end, and explain the mathematical foundations and assumptions behind them
  • Hands-on experience designing and validating models for regression, classification and unsupervised learning tasks
  • Deep understanding of bias–variance tradeoff, regularization techniques, and feature selection methods
• Machine Learning Lifecycle Management – Experience delivering end-to-end solutions from data sourcing and preprocessing through model deployment and results interpretation (Advanced):
  • Architect and execute full pipelines—from data ingestion and feature engineering through model training, validation, deployment, monitoring and retraining, using best practices in reproducibility and CI/CD
  • Troubleshoot production issues (drift, latency, scaling) and optimise models for performance and cost
• Agile Methodologies – Ability to work effectively in an agile delivery environment (Intermediate):
  • Participate effectively in sprint planning, daily stand-ups and retrospectives
  • Break work into user stories, estimate tasks and collaborate with product owners to groom the backlog
• Requirements Gathering & Translation – Skill in partnering with product owners to translate business needs into data science requirements and success metrics (Advanced):
  • Lead interactions with stakeholders to outline clear business objectives and translate them into measurable data science success metrics.
  • Draft technical specifications and align on KPIs, risk factors and roadmap milestones
• Data Science Project Execution – Demonstrable track record of completing data science projects (professional, academic or personal) with a clear business focus (Advanced):
  • Own multiple data science projects from proof-of-concept through delivery, ensuring alignment with business value and timelines
  • Document methodologies, maintain reproducible codebases and present actionable insights to senior leadership
• Python Programming – Strong programming skills in Python, including libraries like pandas, NumPy, scikit-learn and others for data manipulation and modeling (Advanced):
  • Write clean, modular, well-tested Python code
  • Build custom utilities or packages, optimize critical code paths (vectorization, parallelism) and manage dependencies
• SQL Querying & Data Manipulation – Practical knowledge of SQL for extracting, transforming and loading data from relational databases (Intermediate):
  • Extract and join complex datasets from relational databases, write performant queries (window functions, CTEs) and perform ETL tasks
• Version Control & Git – Proficiency with Git for source code management, branching strategies, merging, and collaborative workflows (Intermediate):
  • Use feature branching, pull requests and code reviews in a team setting
• Data Science Communication – Ability to articulate complex data science concepts and results clearly to both technical and non-technical stakeholders (Intermediate):
  • Craft clear, concise narratives around model design, performance and business impact for both technical and non-technical audiences
  • Design and deliver visuals (e.g. dashboards, slide decks, annotated charts) that guide stakeholders through your methodology, results and recommended actions
• Team Collaboration & Knowledge Sharing – Enjoy working in cross-functional teams and learning from peers, contributing to collective problem-solving (Intermediate):
  • Mentor junior engineers and foster a culture of continuous learning
  • Contribute to peer code reviews, internal tech talks or knowledge sharing sessions

Nice to have:

• Deep Learning Frameworks – Proficiency with frameworks such as TensorFlow, PyTorch, Keras, Theano or CNTK for building and training neural networks (Intermediate)
• Cloud Computing Platforms – Experience working in cloud environments (Azure, GCP or AWS), including managing resources, pipelines and scalable deployments (Intermediate)
• Privacy Enhancing Techniques (PETs) – Some experience with homomorphic encryption, federated learning, differential privacy etc. (Intermediate)

Relevant experience areas:

• Machine Learning
• Generative AI
• MLOps & CI/CD
• Cloud Native ML Services

Degrees/Field of Study required:

Not specified

Degrees/Field of Study preferred:

Not specified

Certifications:

Not specified

Required Skills:

Accepting Feedback, Active Listening, AI Implementation, Analytical Thinking, C++ Programming Language, Communication, Complex Data Analysis, Creativity, Data Analysis, Data Infrastructure, Data Integration, Data Modeling, Data Pipeline, Data Quality, Deep Learning, Embracing Change, Emotional Regulation, Empathy, GPU Programming, Inclusion, Intellectual Curiosity, Java (Programming Language), Learning Agility, Machine Learning {+ 26 more}

Optional Skills:

Accepting Feedback

How to Apply:

No application instructions were provided in the job description.

General Conditions:

No general conditions were provided in the job description.
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