Data Scientist Lead - Manager

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

A career in Products and Technology is an opportunity to bring PwC's strategy to life by driving products and technology into everything we deliver. Our clients expect us to bring the right people and the right technology to solve their biggest problems; Products and Technology is here to help PwC meet that challenge and accelerate the growth of our business. We have skilled technologists, data scientists, product managers and business strategists who are using technology to accelerate change. Our team designs, develops and programs the methods, processes, and systems that are used to collect all forms of data and develop models that serve predictions to applications, automated process flows, and stakeholders. A Data Scientist collects domain context from stakeholders, defines hypothesis and prediction tasks, identifies and creates supporting data sources, conducts experiments with various algorithms to model prediction tasks, undertakes validation and tests of models to improve performance, produces pipelines that can be used to automate training and predictions with unseen or production data, identifies meaningful insights from data sources, and contextualizes model outputs to communicate with stakeholders (product owners, process managers, and end consumers).

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, recognize their strengths, and encourage them to take ownership of their personal 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 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 summarizing key points.
• Uphold the firm's code of ethics and business conduct.

Job Requirements and Preferences:

Basic Qualifications:

Minimum Degree Required: Bachelor Degree

Additional Educational Requirements: Bachelor's degree or in lieu of a degree, 10 years of professional experience involving technology-focused process improvements, transformations, and/or system implementations.

Minimum Years of Experience: 6-8 year(s)

Preferred Qualifications:

Degree Preferred: Master Degree

Preferred Field of Study: Computer and Information Science, Mathematics, Computer Engineering, Artificial Intelligence and Robotics, Statistics, Data Processing/Analytics/Science, Mathematical Statistics

Preferred Knowledge/Skills:

• Demonstrates extensive knowledge and/or a proven record of success in applied subject matter:
• Leading, training and working with other data scientists in designing effective analytical approaches taking into consideration performance and scalability to large datasets;
• Demonstrating proven experience in managing stakeholders (e.g., executive level leadership) relationships related to various projects;
• Understanding of ETL tools and techniques, such as tools like Azure Data Factory, Snaplogic Talend, Mapforce, etc. - 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; 
• Demonstrating extensive abilities and/or a proven record of success in the application of statistical modeling, algorithms, data mining and machine learning algorithms problem solving;
• Demonstrating a track record of delivery within a number of large scale projects, demonstrating ownership of architecture solutions and managing change; 
• Manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources; 
• Demonstrating 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; 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:

• Utilizing and applying knowledge commonly used data science packages including Spark, Pandas, SciPy, and Numpy;
• Possessing 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;
• 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 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; 
• Using common cloud computing platforms including Azure, AWS and GCP in addition to their respective utilities for managing and manipulating large data sources, model, development, and deployment; and, 
• Strong familiarity with Machine Learning cloud services including Azure Cognitive Services
• Having expertise in recommender systems is highly desired.

At PwC, our work model includes three ways of working: virtual, in-person, and flex (a hybrid of in-person and virtual). Visit the following link to learn more: https://pwc.to/ways-we-work.

PwC does not intend to hire experienced or entry level job seekers who will need, now or in the future, PwC sponsorship through the H-1B lottery, except as set forth within the following policy: https://pwc.to/H-1B-Lottery-Policy.