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PwC Labs – Senior Data Scientist 59 views

PwC Professional skills and responsibilities for this management level include but are not limited to

As a Manager, you’ll work as part of a team of problem solvers, helping to solve complex business issues from strategy to execution.

  • Pursue opportunities to develop existing and new skills outside of the comfort zone.
  • Act to resolve issues that prevent effective team working, even during times of change and uncertainty.
  • Coach others and encourage them to take ownership of their development.
  • Analyze 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 the firm’s/client’s expectations.
  • Develop a perspective on key global trends, including globalization, 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.

Additional Educational Requirements

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

Minimum Years Of Experience

4 year(s)

Degree Preferred

Preferred Qualifications:

Master Degree

Preferred Fields Of Study

Artificial Intelligence and Robotics, Computer and Information Science, Mathematics, Mathematical Statistics, Statistics, Data Processing/Analytics/Science, Computer Engineering

Certification(s) Preferred

PMI Certified, Agile Certified Scrum Master

Preferred Knowledge/Skills

Demonstrates extensive knowledge and/or a proven record of success in the following areas:

  • 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 Talent, Mapforce, how to map transformation and flow of data from a source to a target system;
  • Performing in development language environments: e.g. Python, Java, Scala, C++, R, SQL, etc. and applying analytical methods to large and complex datasets leveraging one of those languages;
  • Applying statistical modeling, algorithms, data mining, and machine learning algorithms problem solving;
  • Managing business development such as client relationship management and leading and contributing to client proposals;
  • Delivering and tracking successfully large-scale projects, including 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;
  • Demonstrating proven ability with NLP and text-based extraction techniques;
  • Developing data science analytic models and simultaneously operationalizing 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/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;
  • Demonstrating a familiarity with thorough 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 results obtained;
  • Developing end to end deep learning solutions for structured and unstructured data problems;
  • Developing and deploying AI 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;
  • 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; and,
  • Visualizing and communicating analytical results, using technologies such as HTML, JavaScript, D3, Tableau, and PowerBI.

All qualified applicants will receive consideration for employment at PwC without regard to race; creed; color; religion; national origin; sex; age; disability; sexual orientation; gender identity or expression; genetic predisposition or carrier status; veteran, marital, or citizenship status; or any other status protected by law. PwC is proud to be an affirmative action and equal opportunity employer.

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