Machine Learning Engineer - Machine Learning

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. Hands-on experience with regression, classification, clustering and other classical statistical models and algorithms – Must have – Advanced

• 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 – Must have – 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, participating in sprint planning, stand-ups and retrospectives – Must have – 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 – Must have – 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 – Must have – 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 – Must have – 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 – Must have – 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 – Must have – 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 – Must have – 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 – Must have – 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,