Commercial technology office - data science
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Specialty/Competency: Data Science Industry/Sector: Not Applicable Time Type: Full time Travel Requirements: Up to 20% At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven decision making. You will work on developing predictive models, conducting statistical analysis, and creating data visualisations to solve complex business problems. Driven by curiosity, you are a reliable, contributing member of a team. In our fast-paced environment, you will have the chance to work on a variety of assignments, each presenting varying challenges and scope. Every experience is an opportunity to learn and grow. You are encouraged to ask questions, take initiative, and produce quality work that adds value for our clients and contributes to our team's success. During your time at the Firm, you start to establish your personal brand, paving the way to more opportunities. Skills Examples of the skills, knowledge, and experiences you need to lead and deliver value at this level include but are not limited to:- Apply a learning mindset and take ownership for your own development.
- Appreciate diverse perspectives, needs, and feelings of others.
- Adopt habits to sustain high performance and develop your potential.
- Actively listen, ask questions to check understanding, and clearly express ideas.
- Seek, reflect, act on, and give feedback.
- Gather information from a range of sources to analyse facts and discern patterns.
- Commit to understanding how the business works and building commercial awareness.
- Learn and apply professional and technical standards (e.g. refer to specific PwC tax and audit guidance), uphold the Firm's code of conduct and independence requirements.
- Supporting data science and machine learning engineering teams in various projects
- Participating in the development and implementation of data-driven solutions
- Assisting in data modeling to organize and structure complex datasets for analysis
- Engaging in data mining and data engineering tasks to extract meaningful insights
- Applying machine learning techniques to enhance data-driven decision-making processes
- Utilizing programming languages such as C++ and R for data analysis and model development
- Conducting statistical analysis to identify patterns and trends in large datasets
- Collaborating with team members to integrate artificial intelligence and natural language processing into projects
- Learning and applying data science algorithms and methodologies to support project goals
- Gaining exposure to data science practices and contributing to the team's success through research and basic tasks
- Currently pursuing or have completed a Bachelor's degree
- Client service intern positions are entry-level roles intended for job seekers who are in their third year of a four-year degree program or fourth year of a five-year program at the time of application. Winter internships typically occur during the spring semester preceding the student's final year of school Summer internships typically take place during the summer preceding the student's final year of school
- Preference for one of the following field(s) of study: Artificial Intelligence and Robotics, Business Analytics, Computer and Information Science, Computer Engineering, Computer Programming, Data Processing/Analytics/Science, Engineering, Information Technology, Machine Learning, Management Information Systems, Mathematics, Statistics, Systems Engineering
- Preference for a 3.3 overall GPA
- Demonstrating proficiency in C++ and R programming languages
- Utilizing machine learning techniques for data-driven decision making
- Applying data modeling skills to organize and analyze complex datasets
- Engaging in predictive modeling and statistical analysis for insights
- Exploring natural language processing for innovative data solutions
- Supporting project management tasks in data science environments
- Leveraging AI to create efficiencies, innovate ways of working and deliver distinctive outcomes
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