ETIC, Machine Learning, Senior Associate
- Machine Learning
- Natural Language Processing
- AI
- CI/CD
- Agile
- Python
- Pandas
- NumPy
- scikit-learn
- SQL
- ETL
- Git
- TensorFlow
- PyTorch
- Azure
- GCP
- AWS
- MLOps
- C++
- Data Modeling
- Java
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.ResponsibilitiesÂ
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Designing and developing data science and machine learning assets for PwC and its clientsÂ
Contributing effective, useful code to our Data Science codebaseÂ
Participating in constant learning through training and skills developmentÂ
Deploying and managing machine learning models in production environments, ensuring scalability,reliability and performance monitoringÂ
Embedding Responsible AI practices across the model lifecycle, ensuring fairness, transparency, explainability, bias mitigation and compliance with ethical and regulatory standardsÂ
Contributing to the strategy and growth of afast developing data science capabilityÂ
Craft and communicate compelling business “stories” based on analytics insightÂ
Business case and Proposal developmentÂ
Presenting findings to senior internal and external stakeholders Â
Being part of this technology innovation effort of the FirmÂ
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Key Skills RequiredÂ
4+ Years Experience
Statistical Analysis & Machine Learning Theory – Excellent understanding of statistics, machine learning techniques andalgorithms. Hands-on experience with regression, classification, clustering and other classical statistical models and algorithms – Must have – AdvancedÂ
Independently formulate hypotheses, choose and justifyappropriate statistical tests and interpret resultsÂ
Select,implement and tune ML algorithms (e.g. random forests, SVMs, gradient boosting) end-to-end, and explain the mathematical foundations and assumptions behind themÂ
Hands-on experience designing and validating models for regression,classification and unsupervised learning tasksÂ
Deep understanding of bias–variancetradeoff, regularization techniques, and feature selection methodsÂ
Machine Learning Lifecycle Management – Experience delivering end-to-end solutions from data sourcing and preprocessing through model deployment and results interpretation – Must have – AdvancedÂ
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/CDÂ
Troubleshoot production issues (drift, latency, scaling) andoptimise models for performance and costÂ
Agile Methodologies – Ability to work effectively in an agile delivery environment,participating in sprint planning,stand-ups and retrospectives – Must have – IntermediateÂ
Participate effectively in sprint planning, dailystand-ups and retrospectivesÂ
Break work into user stories, estimatetasks and collaborate with product owners to groom the backlogÂ
Requirements Gathering & Translation – Skill in partnering with product owners to translate business needs into data science requirements and success metrics – Must have – AdvancedÂ
Lead interactions with stakeholders to outline clear businessobjectives and translate them into measurable data science success metrics.Â
Draft technical specifications and align on KPIs, risk factors and roadmap milestonesÂ
Data Science Project Execution – Demonstrabletrack record of completing data science projects (professional,academic or personal) with a clear business focus – Must have – AdvancedÂ
Own multiple data science projects from proof-of-concept through delivery, ensuring alignment with business value and timelinesÂ
Document methodologies,maintain reproducible codebases and present actionable insights to senior leadershipÂ
Python Programming – Strong programming skills in Python, including libraries like pandas, NumPy, scikit-learn and others for data manipulation andmodeling – Must have – AdvancedÂ
Write clean, modular, well-tested Python codeÂ
Build custom utilities or packages, optimize critical code paths (vectorization, parallelism) and manage dependenciesÂ
SQL Querying & Data Manipulation – Practical knowledge of SQL for extracting,transforming and loading data from relational databases – Must have – IntermediateÂ
Extract and join complex datasets from relational databases, write performant queries (window functions, CTEs) and perform ETL tasksÂ
Version Control & Git – Proficiency with Git for source code management, branching strategies, merging, and collaborative workflows – Must have – IntermediateÂ
Use feature branching, pullrequests and code reviews in a team settingÂ
Data Science Communication – Ability to articulate complex data science concepts and results clearly to both technical and non-technical stakeholders – Must have – IntermediateÂ
Craft clear, concise narratives around model design,performance and business impact for both technical and non-technical audiencesÂ
Design and deliver visuals (e.g. dashboards, slide decks, annotated charts) that guide stakeholders through yourmethodology,results and recommended actionsÂ
Team Collaboration & Knowledge Sharing – Enjoy working in cross-functional teams and learning from peers, contributing to collective problem-solving – Must have – IntermediateÂ
Mentor junior engineers and foster a culture of continuous learningÂ
Contribute to peer code reviews, internal tech talks or knowledge sharing sessionsÂ
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Nice to haveÂ
Deep Learning Frameworks – Proficiency with frameworks such as TensorFlow,PyTorch,Keras, Theano or CNTK for building and training neural networks –IntermediateÂ
Cloud Computing Platforms – Experience working in cloud environments (Azure, GCP or AWS), including managing resources,pipelines and scalable deployments – IntermediateÂ
Privacy Enhancing Techniques (PETs) – Some experience with homomorphic encryption, federated learning, differential privacyetc. – IntermediateÂ
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Relevant experience areasÂ
Machine Learning, Generative AI,MLOps & CI/CD, Cloud Native ML Services,Â
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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 Skills
Optional 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
ETIC, Machine Learning, Senior Associate · wd3:pwc:Global_Experienced_Careers