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PwC Data Scientist - (PwC Labs) in Tampa, Florida

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.

Responsibilities

As a Senior Associate, you’ll work as part of a team of problem solvers with extensive consulting and industry experience, helping our clients solve their complex business issues from strategy to execution. Specific responsibilities include but are not limited to:

  • Proactively assist in the management of several clients, while reporting to Managers and above

  • Train and lead staff

  • Establish effective working relationships directly with clients

  • Contribute to the development of your own and team’s technical acumen

  • Keep up to date with local and national business and economic issues

  • Be actively involved in business development activities to help identify and research opportunities on new/existing clients

  • Continue to develop internal relationships and your PwC brand

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 :

3 year(s)

Preferred Qualifications :

Degree Preferred :

Master Degree

Preferred Fields of Study :

Business Analytics, Computer and Information Science, Mathematics

Preferred Knowledge/Skills :

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

  • New technology learning and quickly evaluating their technical and commercial viability;

  • Machine learning techniques for addressing a variety of problems (e.g. consumer segmentation, revenue forecasting, image classification, etc.); and,

  • Machine learning algorithms (e.g. k-nearest neighbors, random forests, ensemble methods, deep neural networks, etc.) and when it is appropriate to use each technique. Demonstrates thorough abilities and/or a proven record of success as a team leader including the following areas:

  • New technology learning and quickly evaluating their technical and commercial viability;

  • Machine learning techniques for addressing a variety of problems (e.g. consumer segmentation, revenue forecasting, image classification, etc.); and,

  • Machine learning algorithms (e.g. k-nearest neighbors, random forests, ensemble methods, deep neural networks, etc.) and when it is appropriate to use each technique.

  • Building machine learning models and systems, interpreting their output, and communicating the results;

  • Moving models from development to production; and,

  • Conducting research in a lab and publishing work. Demonstrates thorough abilities and/or a proven record of success with a subset of the following technologies:

  • New technology learning and quickly evaluating their technical and commercial viability;

  • Machine learning techniques for addressing a variety of problems (e.g. consumer segmentation, revenue forecasting, image classification, etc.); and,

  • Machine learning algorithms (e.g. k-nearest neighbors, random forests, ensemble methods, deep neural networks, etc.) and when it is appropriate to use each technique.

  • Building machine learning models and systems, interpreting their output, and communicating the results;

  • Moving models from development to production; and,

  • Conducting research in a lab and publishing work.

  • Programming including Python, R, Java, JavaScript, C++, Unix Hardware, sensors, robotics, GPU enabled machine learning, FPGAs, and Raspberry Pis, etc.;

  • Data Storage Technologies including SQL, NoSQL, Hadoop, cloud-based databases such as GCP BigQuery, and different storage formats (e.g. Parquet, etc.);

  • Data Processing Tools including Python (Numpy, Pandas, etc.), Spark, and cloud-based solutions such as GCP DataFlow;

  • Machine Learning Libraries including Python (scikit-learn, genism, etc.), TensorFlow, Keras, PyTorch, and Spark MLlib;

  • Visualization including Python (Matplotlib, Seaborn, bokeh, etc.), and JavaScript (d3); and,

  • Productionization and containerization technologies including GitHub, Flask, Docker, and Kubernetes.

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.

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.

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