A career in our Data Science practice, within Innovation services, will provide you with the opportunity to help our clients' tax departments redesign, redefine, and redeploy tax to be a strategic asset across the enterprise. You'll focus on assisting clients incorporate increased automation in the tax reporting process, increase analytic capabilities through data integration, and create solid internal controls that will enable the Tax function to deliver better quality output and contribute more strategically to organisational decision making.
Our team focuses on developing cutting edge data analytic and automation applications that provide data and visualisation support for large and small complex data sources.
As a Manager, 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 a portfolio of clients, while reporting to Senior Managers and above
+ Be involved in the financial management of clients
+ Be actively involved in business development activities to help identify and research opportunities on new/existing clients
+ Contribute to the development of your own and team's technical acumen
+ Develop strategies to solve complex technical challenges
+ Assist in the management and delivering of large projects
+ Train, coach, and supervise staff
+ Keep up to date with local and national business and economic issues
+ Continue to develop internal relationships and your PwC brand
**Job Requirements and Preferences** :
**Basic Qualifications** :
**Minimum Degree Required** :
**Minimum Years of Experience** :
**Preferred Qualifications** :
**Preferred Knowledge/Skills** :
Demonstrates thorough knowledge and/or a proven record of success in data analytics and applied subject matter such as finance, accounting, energy, or health care; including the following areas:
- Participating in planning with other data scientists on the most effective analytical approach based upon requirements taking into consideration performance and scalability to large datasets;
- Designing and building analytical procedures;
- Performing unit and system testing to validate the output of the analytic procedures against expected results;
- Understanding of relational databases and SQL, NoSQL database models, XML and other database models, development languages, such as Python, Java or equivalent and applying analytical methods to large datasets leveraging one of those languages;
- Utilizing ETL tools and techniques, such as Talend and Mapforce to map transformation and flow of data from a source to a target system;
- Applying into projects statistical or numerical methods, data munging or data-driven problem solving;
- Utilizing techniques such as multivariate regressions, Bayesian probabilities, clustering algorithms, machine learning, dynamic programming, stochastic-processes, queueing theory, algorithmic knowledge to efficiently research and solve complex development problems and application of engineering methods to define, predict and evaluate the results obtained;
- Using large data sets, along with analytical scripting tools and visualization platforms to produce actionable insights for clients, data cleansing, transformation, and modeling in order to produce a clear story that is easily comprehended by non-technical audiences;
- Developing solutions through data analytics and programming/scripting utilizing Python, Java, R, Scala, and C++;
- Programming skills and knowledge of how to write models which can be directly used in production as part of a large scale system;
- Leveraging knowledge of data wrangling techniques and scripting languages; - Working on a cloud based infrastructure environment;
- Understanding how to develop data science analytic models to operationalize these models so they can run in an automated context;
- Understanding of machine learning algorithms, such as k-NN, GBM, Neural Networks Naive Bayes, SVM, Decision Forests, etc., and,
- Utilizing and applying into projects knowledge of technologies such as H20.ai, Google Machine Learning and deep learning; NLP and text based extraction techniques.
Demonstrates thorough abilities and/or a proven record of success in the following areas:
- Creating models which can be productionised immediately;
- Communicating complex engineering concepts succinctly to senior non-technical and technical decision makers;
- Working cross-functionally with multiple teams and stakeholders to juggle multiple time-sensitive projects efficiently;
- Managing accounts, follow-through, resourcefulness, and attention to detail;
- Applying moderately complex mathematical or statistical methodologies;
- Utilizing effective written and verbal business communication skills when interacting with team members and/or clients in a professional setting.
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.
Associated topics: artificial intelligence, circuit, c++, electrical engineering, electronic engineering, linux, matlab, python, robotics, software