At PwC, we respect and value differences. When people from different backgrounds and with different points of view work together, we create the most value – for our clients, our people and society. Learn more
PricewaterhouseCoopers (PwC) is an accounting company headquartered in the New York City, NY area with 5001 to 10000 employees. PricewaterhouseCoopers (PwC) has a 3.9-star InHerSight Score, based on 5,981 ratings from 401 current or former employees. 53 employees have left comments about their experience working for the company on InHerSight.
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 Automation Lab within PwC Labs is focused on implementing intelligent process automation solutions that will impact the overall efficiency and effectiveness of our business processes across Tax, Assurance, Advisory, and Internal Firm Services. Process improvement, transformation, system implementation, effective use of technology and data & analytics, and leveraging alternative delivery solutions are key areas of focus to drive additional value to our firm.
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 an authentic and inclusive leader, at all grades/levels and in all lines of service. 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 Manager, 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:
Pursue opportunities to develop existing and new skills outside of comfort zone.
Act to resolve issues which prevent effective team working, even during times of change and uncertainty.
Coach others and encourage them to take ownership of their development.
Analyse complex ideas or proposals and build a range of meaningful recommendations.
Use multiple sources of information including broader stakeholder views to develop solutions and recommendations.
Address sub-standard work or work that does not meet firm's/client's expectations.
Develop a perspective on key global trends, including globalisation, and how they impact the firm and our clients.
Manage a variety of viewpoints to build consensus and create positive outcomes for all parties.
Focus on building trusted relationships.
Uphold the firm's code of ethics and business conduct.
The Lead Data Scientist will design and develop data science, machine learning, natural language processing and related solutions to address business needs. They will 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, and lead/mentor junior data scientists.
Job Requirements and Preferences:
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: 4 year(s)
Demonstrates extensive knowledge and/or a proven record of success in data analytics, including the following areas:
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;
Understanding of NoSQL (Graph, Document, Columnar) database models, XML, relational and other database models and associated SQL;
Utilizing and applying knowledge commonly used data science packages including Spark, Pandas, SciPy, and Numpy;
Understanding of ETL tools and techniques, such as tools like Talend, Mapforce, how tomap transformation and flow of data from a source to a target system;
Creating and applying statistical modelling, algorithms, data mining and machine learning algorithms to problem solve;
Delivering on 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;
Manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources;
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; and,
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:
Understanding NLP and text based extraction techniques;
Applying 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, clusteringalgorithms, machine learning, dynamic programming, stochastic-processes, queuingtheory, algorithmic knowledge to efficiently research and solve complex developmentproblems and application of engineering methods to define, predict and evaluate theresults obtained;
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;
Working creatively and analytically to apply cutting edge techniques to specific challenges; and,
Staying current on the latest AI trends, tools, methodologies, and techniques.