About The Role

Industry/Sector

Not Applicable

Specialism

Data Science

Management Level

Manager

Job Description & Summary

The Opportunity

Join our Acceleration Center Manila and help shape the future of business for our diverse client portfolio across geographies and jurisdictions. You’ll work at the heart of global teams across Advisory, Assurance, Tax and Business Services—solving real client challenges through connected collaboration. We’ll help you grow your skills so you can go further. With hands-on learning, cutting-edge tools and an inclusive culture, this is your opportunity to do inspiring work that makes a difference—every day.

As an AI Engineer Manager, you will lead multidisciplinary teams in the design, development, and deployment of data-driven solutions, transforming raw data into actionable insights that drive business growth. Within our Advisory practice, you will leverage advanced analytics and statistical techniques to extract insights from large datasets, enabling informed decision-making and solving complex business problems.

As a Manager, you will enhance your leadership style by motivating and developing your team to deliver quality results. You will be responsible for coaching and leveraging team members' unique strengths to meet client expectations. With your growing business knowledge, you will play a crucial role in identifying opportunities that contribute to the success of our firm. You are expected to lead with integrity and authenticity, articulating our purpose and values meaningfully.

In this role at PwC Acceleration Center Manila, you will take ownership of projects, confirming their successful planning, budgeting, execution, and completion. You will embrace technology and innovation to enhance your delivery and encourage others to do the same, fostering a culture of continuous improvement and excellence.

Responsibilities

Leading data science initiatives to transform raw data into actionable insights for informed decision-making
Guiding teams in the development and deployment of advanced data solutions using machine learning and artificial intelligence
Utilizing statistical modeling and predictive analytics to address complex business challenges
Designing and implementing data pipelines and data lakes to support large-scale data processing
Overseeing the creation of data visualizations to communicate insights effectively to stakeholders
Managing project timelines and budgets to deliver data-driven solutions on schedule
Coaching team members to enhance their skills in data science and analytics
Collaborating with business leaders to identify opportunities for leveraging data to drive growth
Promoting the adoption of innovative technologies and methodologies in data engineering and analytics
Confirming data quality and compliance within analytics frameworks to maintain integrity

What You Must Have

At least a Bachelor's degree
At least 7 to 11 years of experience
Oral and written proficiency in English required
Strong full-stack or backend development skills, including Python, Angular or React, RESTful APIs, microservices, SQL/NoSQL databases, testing, and software architecture.
Hands-on experience building AI-integrated applications, using LLMs, RAG, embeddings, vector databases, and frameworks such as LangChain, LlamaIndex, or Autogen.
Proven cloud and DevOps capability, including Azure, AWS, or GCP deployment, Docker, Kubernetes, CI/CD, Git, security, reliability, and scalability.

What Sets You Apart

Preference for at least one of the following fields 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
Demonstrating proficiency in data science algorithms and workflows
Utilizing machine learning and deep learning techniques effectively
Excelling in complex data analysis and predictive modeling
Managing data pipelines and confirming data quality
Leveraging programming languages such as Python, R, and MATLAB
Embracing change and innovation in technology-driven environments