Data Science - Manager

At PwC, our people in data and analytics focus on leveraging data to drive insights and make informed business decisions. They utilise advanced analytics techniques to help clients optimise their operations and achieve their strategic goals.

In data analysis at PwC, you will focus on utilising advanced analytical techniques to extract insights from large datasets and drive data-driven decision-making. You will leverage skills in data manipulation, visualisation, and statistical modelling to support clients in solving complex business problems.

Enhancing your leadership style, you motivate, develop and inspire others to deliver quality. You are responsible for coaching, leveraging team member’s unique strengths, and managing performance to deliver on client expectations. With your growing knowledge of how business works, you play an important 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 in a meaningful way. You embrace technology and innovation to enhance your delivery and encourage others to do the same.

Skills

Examples of the skills, knowledge, and experiences you need to lead and deliver value at this level include but are not limited to:

• Analyse and identify the linkages and interactions between the component parts of an entire system.
• Take ownership of projects, ensuring their successful planning, budgeting, execution, and completion.
• Partner with team leadership to ensure collective ownership of quality, timelines, and deliverables.
• Develop skills outside your comfort zone, and encourage others to do the same.
• Effectively mentor others.
• Use the review of work as an opportunity to deepen the expertise of team members.
• Address conflicts or issues, engaging in difficult conversations with clients, team members and other stakeholders, escalating where appropriate.
• Uphold and reinforce professional and technical standards (e.g. refer to specific PwC tax and audit guidance), the Firm's code of conduct, and independence requirements.
  
  

The Opportunity

When you join PwC Acceleration Centers (ACs), you step into a pivotal role focused on actively supporting various Acceleration Center services, from Advisory to Assurance, Tax and Business Services. In our innovative hubs, you’ll engage in challenging projects and provide distinctive services to support client engagements through enhanced quality and innovation. You’ll also participate in dynamic and digitally enabled training that is designed to grow your technical and professional skills.

As part of the Data Science team you shall lead the creation and implementation of impactful data science solutions. As a Manager you shall guide the execution of our data science strategy, maintaining a customer-centric approach that supports PwC's goals and vision.Responsibilities

Lead the development and implementation of data science solutions

Guide the execution of data science strategies with a client-focused approach

Confirm alignment with PwC's goals and vision

Supervise and mentor team members to deliver top-quality results

Utilize firm methodologies and technology to enhance client service

Foster powerful client relationships and manage complex engagements

Maintain elevated standards of integrity and professionalism

Identify opportunities for process improvements and innovationWhat You Must Have

Bachelor's & Master's Degree in Computer Science, Engineering, Econometrics, Statistics, Business Analytics, Informatics- 8 years of experience

Oral and written proficiency in English requiredWhat Sets You Apart

Experience in building & deploying ML models in cloud environments

Knowledge and experience of supervised, unsupervised machine learning algorithms

Knowledge of statistics

Experience in Data analysis

Experience in machine learning frameworks and tools

Advanced level programming in SQL and Python/Pyspark

Proficiency with visualization tools

Working knowledge of containerization

Experience with model explainability and interpretability techniques

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