Line of Service
Advisory - Other
Job Description & Summary
A career in our Advisory Acceleration Centre is the natural extension of PwC’s leading class global delivery capabilities. We provide premium, cost effective, high quality services that support process quality and delivery capability in support for client engagements.
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. If as a
professional you are looking to put your skills to work in a product-based, fast paced, entrepreneurial, and
inclusive environment, PwC Labs is the team for you.
A career in our PwC Labs, will provide you with a unique opportunity to build transformative products and
innovate mechanisms that bring new insights to our business and customers that can help identify
business gaps, solve problems, and build new business opportunities.
Design and develop data science, machine learning, natural language processing, deep learning and related solutions to address business needs
Work creatively and analytically to apply cutting edge techniques to specific challenges
Assist in the management and delivery of large data science projects
Work with a wide range of automation teams to validate findings and proposed analytics solutions
Continuously expand personal skill sets and stay up to speed on the latest A.I. trends, tools, methodologies, and techniques
Skills and Experience:
Demonstrates extensive knowledge and/or a proven record of success in data analytics, including the following areas:
Ideally 6 to 9 years of relevant experience
Bachelor’s Degree in Computer Science, Engineering or other technical discipline (BE, BTech, MCA).
Performing in development language environments- e.g. Python, Java, Scala, R, SQL, etc. and applying analytical methods to large and complex datasets leveraging one of those languages
Experience in machine learning, natural language processing, deep learning
Understanding of NoSQL (Graph, Document, Columnar) database models, XML, relational and other database models and associated SQL
Understanding of ETL tools and techniques, such as tools like Talend, Map force, how to
map transformation and flow of data from a source to a target system
Demonstrates extensive abilities and/or a proven record of success in the application of statistical modelling, algorithms, data mining and machine learning algorithms problem solving
A track record of delivery within a number of large-scale projects, demonstrating ownership of architecture solutions and managing change
Leading, training and working with other data scientists in designing effective analytical approaches taking into consideration performance and scalability to large datasets
Experience manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources
Proven ability with NLP and text-based extraction techniques
Understanding of not only how to develop data science analytic models but how to operationalize these models so they can run in an automated context
Understanding of machine learning algorithms, such as k-NN, GBM, Neural Networks Naive Bayes, SVM, and Decision Forests
Demonstrates extensive abilities and/or a proven record of success in the application of statistical or numerical methods, data mining or data-driven problem solving, including the following areas:
Utilizing and applying knowledge commonly used data science packages including Spark, Pandas, SciPy, and Numpy.
Familiarity with deep learning architectures used for text analysis, computer vision and signal processing.
Utilizing programming skills and knowledge on how to write models which can be directly
used in production as part of a large-scale system.
Utilizing and applying knowledge of technologies such as H20.ai, Google Machine Learning and Deep learning.
Applying techniques such as multivariate regressions, Bayesian probabilities, clustering
algorithms, machine learning, dynamic programming, stochastic-processes, queuing
theory, algorithmic knowledge to efficiently research and solve complex development
problems and application of engineering methods to define, predict and evaluate the
Developing end to end deep learning solutions for structured and unstructured data problems.
Developing and deploying A.I. solutions as part of a larger automation pipeline
Using common cloud computing platforms including AWS and GCP in addition to their respective utilities for managing and manipulating large data sources, model, development, and deployment.
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)
Desired Languages (If blank, desired languages not specified)
Available for Work Visa Sponsorship?
Government Clearance Required?
Job Posting End Date
October 15, 2021