Line of Service AdvisoryIndustry/Sector TechnologySpecialism Advisory - OtherManagement Level Senior AssociateJob Description & Summary 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.ResponsibilitiesDesigning and developing data science and machine learning assets for PwC and its clientsContributing effective, useful code to our Data Science codebaseParticipating in constant learning through training and skills developmentDeploying and managing machine learning models in production environments, ensuring scalability, reliability and performance monitoringEmbedding Responsible AI practices across the model lifecycle, ensuring fairness, transparency, explainability, bias mitigation and compliance with ethical and regulatory standardsContributing to the strategy and growth of a fast developing data science capabilityCraft and communicate compelling business “stories” based on analytics insightBusiness case and Proposal developmentPresenting findings to senior internal and external stakeholdersBeing part of this technology innovation effort of the FirmKey Skills Required4+ Years ExperienceStatistical 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 – AdvancedIndependently formulate hypotheses, choose and justify appropriate statistical tests and interpret resultsSelect, implement and tune ML algorithms (e.g. random forests, SVMs, gradient boosting) end-to-end, and explain the mathematical foundations and assumptions behind themHands-on experience designing and validating models for regression, classification and unsupervised learning tasksDeep understanding of bias–variance tradeoff, regularization techniques, and feature selection methodsMachine Learning Lifecycle Management – Experience delivering end-to-end solutions from data sourcing and preprocessing through model deployment and results interpretation – Must have – AdvancedArchitect 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/CDTroubleshoot production issues (drift, latency, scaling) and optimise models for performance and costAgile Methodologies – Ability to work effectively in an agile delivery environment, participating in sprint planning, stand-ups and retrospectives – Must have – IntermediateParticipate effectively in sprint planning, daily stand-ups and retrospectivesBreak work into user stories, estimate tasks and collaborate with product owners to groom the backlogRequirements Gathering & Translation – Skill in partnering with product owners to translate business needs into data science requirements and success metrics – Must have – AdvancedLead 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 milestonesData Science Project Execution – Demonstrable track record of completing data science projects (professional, academic or personal) with a clear business focus – Must have – AdvancedOwn multiple data science projects from proof-of-concept through delivery, ensuring alignment with business value and timelinesDocument methodologies, maintain reproducible codebases and present actionable insights to senior leadershipPython Programming – Strong programming skills in Python, including libraries like pandas, NumPy, scikit-learn and others for data manipulation and modeling – Must have – AdvancedWrite clean, modular, well-tested Python codeBuild custom utilities or packages, optimize critical code paths (vectorization, parallelism) and manage dependenciesSQL Querying & Data Manipulation – Practical knowledge of SQL for extracting, transforming and loading data from relational databases – Must have – IntermediateExtract and join complex datasets from relational databases, write performant queries (window functions, CTEs) and perform ETL tasksVersion Control & Git – Proficiency with Git for source code management, branching strategies, merging, and collaborative workflows – Must have – IntermediateUse feature branching, pull requests and code reviews in a team settingData Science Communication – Ability to articulate complex data science concepts and results clearly to both technical and non-technical stakeholders – Must have – IntermediateCraft clear, concise narratives around model design, performance and business impact for both technical and non-technical audiencesDesign and deliver visuals (e.g. dashboards, slide decks, annotated charts) that guide stakeholders through your methodology, results and recommended actionsTeam Collaboration & Knowledge Sharing – Enjoy working in cross-functional teams and learning from peers, contributing to collective problem-solving – Must have – IntermediateMentor junior engineers and foster a culture of continuous learningContribute to peer code reviews, internal tech talks or knowledge sharing sessionsNice to haveDeep Learning Frameworks – Proficiency with frameworks such as TensorFlow, PyTorch, Keras, Theano or CNTK for building and training neural networks – IntermediateCloud Computing Platforms – Experience working in cloud environments (Azure, GCP or AWS), including managing resources, pipelines and scalable deployments – IntermediatePrivacy Enhancing Techniques (PETs) – Some experience with homomorphic encryption, federated learning, differential privacy etc. – IntermediateRelevant experience areasMachine Learning, Generative AI, MLOps & CI/CD, Cloud Native ML Services,Education (if blank, degree and/or field of study not specified) Degrees/Field of Study required:Degrees/Field of Study preferred:Certifications (if blank, certifications not specified)Required SkillsOptional Skills Accepting Feedback, 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}Desired Languages (If blank, desired languages not specified)Travel Requirements 0%Available for Work Visa Sponsorship? NoGovernment Clearance Required? NoJob Posting End Date
