ETIC, Machine Learning, Senior Associate
PwC is hiring an ETIC, Machine Learning, Senior Associate in Cairo, Egypt. Level rates it ; you can apply on PwC.
Level's read on this job
- Builds AI ●●●● is how Level rates this role: the job is building AI systems.
- PwC mentions visa sponsorship in this posting.
- Tools and skills the posting cites include PyTorch, TensorFlow and scikit-learn.
- Level classifies it as a senior role.
- Level lists 3,700+ other open roles at PwC.
AI in this role
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.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,
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
How we rate this
ETIC, Machine Learning, Senior Associate at PwC rates 94 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How do you monitor a model once it's live, and how do you know it needs retraining?
- What NLP problem have you worked on, and how did you measure whether it actually worked?
- How do you think about the risk of an AI system in this kind of role failing silently?
- What's a project where you used PyTorch hands-on?
- Walk me through how you've used TensorFlow in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: ML Ops, NLP, AI Safety, PyTorch, and TensorFlow. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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