PwC Labs is focused on standardizing, automating, delivering tools and processes and exploring emerging technologies that drive efficiency and enable our people to reimagine the possible. Process improvement, transformation, effective use of innovative technology and data & analytics, and leveraging alternative delivery solutions are key areas of focus to drive additional value for our firm. The AI Lab focuses on implementing solutions that impact efficiency and effectiveness of our technology functions. Process improvement, transformation, effective use of technology and data & analytics, and leveraging alternative delivery are key areas to drive value and continue to be recognized as the leading professional services firm. AI Lab is focused on identifying and prioritizing emerging technologies to get the most out of our investments.

To really stand out and make us fit for the future in a constantly changing world, each and every one of us at PwC needs to be a purpose-led and values-driven leader at every level. To help us achieve this we have the PwC Professional; our global leadership development framework. It gives us a single set of expectations across our lines, geographies and career paths, and provides transparency on the skills we need as individuals to be successful and progress in our careers, now and in the future.

As a Senior Associate, you'll work as part of a team of problem solvers, helping to solve complex business issues from strategy to execution. PwC Professional skills and responsibilities for this management level include but are not limited to:

• Use feedback and reflection to develop self awareness, personal strengths and address development areas.
• Delegate to others to provide stretch opportunities, coaching them to deliver results.
• Demonstrate critical thinking and the ability to bring order to unstructured problems.
• Use a broad range of tools and techniques to extract insights from current industry or sector trends.
• Review your work and that of others for quality, accuracy and relevance.
• Know how and when to use tools available for a given situation and can explain the reasons for this choice.
• Seek and embrace opportunities which give exposure to different situations, environments and perspectives.
• Use straightforward communication, in a structured way, when influencing and connecting with others.
• Able to read situations and modify behavior to build quality relationships.
• Uphold the firm's code of ethics and business conduct.

Our mandate is to quickly explore new technologies to determine what is relevant for our clients and Firm to invest in. Our work has a tremendous impact on how PwC and our clients do business, whether we are streamlining workflows with ML models or helping clients make strategic investments in Emerging Technologies. Our Data Scientists possess exceptional technical prowess matched by their ability to communicate results to other data scientists, clients, and internal stakeholders.

Job Requirements and Preferences:

Basic Qualifications:

Minimum Degree Required:
Bachelor Degree

Additional Educational Requirements:

In lieu of a Bachelor Degree, 12 years of professional experience involving technology-focused process improvements, transformations, and/or system implementations

Minimum Years of Experience:
2 year(s)

Preferred Qualifications:

Degree Preferred:
Master Degree

Preferred Fields of Study:
Computer and Information Science, Mathematics, Computer Engineering, Artificial Intelligence and Robotics, Mathematical Statistics, Statistics, Economics, Operations Management/Research

Additional Educational Preferences:

PhD highly preferred

Preferred Knowledge/Skills:

Demonstrates thorough abilities and/or a proven record of success:

• Explore new analytical technologies and evaluate their technical and commercial viability;
• Work across entire pipeline: data ingestion, feature engineering, ML model development, visualization of results, and packaging solutions into applications/production ready tools;
• Work across various data mediums: text, audio, imagery, sensory, and structured data;
• Work in (6) 2-week sprint cycles to develop proof-of-concepts and prototype models that can be demoed and explained to data scientists, internal stakeholders, and clients;
• Test and reject hypotheses around data processing and ML model building;
• Experiment, fail quickly, and recognize when you need assistance vs. concluding a technology is not suitable for the task;
• Build ML pipelines that ingest, clean data, and make predictions;
• Focus on AI and ML techniques that are broadly applicable across all industries;
• Stay abreast of new AI research from leading labs by reading papers and experimenting with code; and,
• Develop innovative solutions and perspectives on AI that can be published in academic journals/arXiv and shared with clients.

Demonstrates thorough abilities and/or a proven record of success addressing client needs:

• Continuously learning new technologies and quickly evaluating their technical and commercial viability;
• Apply ML techniques to address a variety of problems (e.g. consumer segmentation, revenue forecasting, image classification, etc.);
• Understanding ML algorithms (e.g. k-nearest neighbors, random forests, ensemble methods, deep neural networks, etc.) and when it is appropriate to use each technique;
• Understanding open-source deep learning frameworks (PyTorch, Keras, Tensorflow);
• Understanding text pre-processing and normalization techniques, such as tokenization, POS tagging and knowledge of Named Entity Extraction, Document Classification, Topic Modeling, Text summarization and concepts behind application;
• Building ML models and systems, interpreting their output, and communicating the results; and,
• Moving models from development to production and conducting lab research and publishing work.

Demonstrates thorough abilities and/or a proven record of success in the Essential 8: AI, Blockchain, Augmented Reality, Drones, IoT, Robotics, Virtual Reality and 3D printing and a subset of these technologies:

• Programming: Python, R, Java, JavaScript, C++, Unix;
• Data Storage Technologies: SQL, NoSQL, Postgres, Neo4j, Hadoop, cloud-based databases such as GCP BigQuery, and different storage formats (e.g. Parquet, etc.);
• Data Processing Tools: Python (Numpy, Pandas, etc.), Spark, cloud-based solutions such as GCP DataFlow;
• Machine Learning Libraries: Python (scikit-learn, genism, etc.), TensorFlow, Keras, PyTorch, Spark MLlib, NLTK, spaCy;
• NLU/NLP domain: Sentiment Analysis, Chatbots & Virtual Assistants, Text Classification, Text Extraction, Machine Translation, Text Summarization, Intent Classification, Speech Recognition, STT, TTS;
• Visualization: Python (Matplotlib, Seaborn, bokeh, etc.), JavaScript (d3), third party libraries (Power BI, Tableau, Data Studio); and,
• Productionization and containerization technologies: GitHub, Flask, Docker, Kubernetes, Azure DevOps, GCP, Azure, AWS.