Data Scientist - TMT Manager Consultant at PwC International


A career in our Analytics Innovation practice, within Analytics, will provide you with the opportunity to combine consulting and industry expertise with data science capabilities. We use descriptive and predictive analytical techniques to incorporate client, third-party, and proprietary data to help answer questions and design solutions to our clients most pressing business issues. Our team helps leverage data to rapidly discover, quantify, and deliver value from data with intelligent analytics and scalable end-to-end business solutions. This includes helping our clients drive analytics adoption, accelerating value delivery, developing in house talent, and building solutions and trust in data.
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 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:
  • Develop new skills outside of comfort zone.
  • Act to resolve issues which prevent the team working effectively.
  • Coach others, recognise their strengths, and encourage them to take ownership of their personal 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.
  • Use data and insights to inform conclusions and support decision-making.
  • Develop a point of view on key global trends, and how they impact clients.
  • Manage a variety of viewpoints to build consensus and create positive outcomes for all parties.
  • Simplify complex messages, highlighting and summarising key points.
  • Uphold the firm's code of ethics and business conduct.

Job Requirements and Preferences:
Basic Qualifications:
Minimum Degree Required:
Bachelor Degree
Required Fields of Study:
Computer and Information Science, Computer and Information Science & Accounting, Economics, Economics and Finance, Economics and Finance & Technology, Engineering, Operations Management/Research, Statistics, Mathematics
Minimum Years of Experience:
6 year(s)
Preferred Qualifications:
Degree Preferred:
Master Degree
Preferred Fields of Study:
Computer and Information Science, Computer and Information Science & Accounting, Economics, Economics and Finance, Economics and Finance & Technology, Engineering, Operations Management/Research, Statistics, Mathematics
Preferred Knowledge/Skills:
Demonstrates extensive knowledge and/or a proven record of success in the following areas:
  • Statistical or numerical methods applications, data mining or data-driven problem solving;
  • Statistical modelling, algorithms, data mining and machine learning algorithms;
  • Business development such as client relationship management and leading and contributing to client proposals; and,
  • Delivery within a number of large scale projects, demonstrating ownership of architecture solutions and managing change.

Demonstrates extensive knowledge and/or a proven record of success in the following areas:
  • Communicate project findings orally and visually to both technical and executive audiences;
  • Develop people through effectively supervising, coaching, and mentoring staff;
  • Lead, train, and work with other data scientists in designing effective analytical approaches taking into consideration performance and scalability to large datasets; and,
  • Manipulate and analyze complex, high-volume, high-dimensionality data from varying sources.

Demonstrates extensive abilities and/or a proven record of success in the following areas:
  • Demonstrating fluency in commonly used data science packages including Spark, Pandas, SciPy, and Numpy;
  • Leveraging familiarity with deep learning architectures used for text analysis, computer vision and signal processing;
  • 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;
  • Utilizing programming skills and knowledge on how to write models which can be directly used in production as part of a large scale system;
  • 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;
  • Using Data visualization software such as tableau and qlikview in addition to web visualization libraries; and,
  • 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.

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