Senior Associate Data and Analytics at PricewaterhouseCoopers (PwC)

Job Description & Summary

• As a Senior Data and Analytics Consultant, you will play a key role in driving data-driven decision-making and leveraging data assets to support business objectives.
• This role encompasses responsibilities across data engineering and architecture, data science and machine learning, data governance and strategy, as well as facilitating and training on data and analytics tools and capabilities.
• You will work closely with cross-functional teams to develop and implement data-driven solutions, optimize data infrastructure, and drive insights and innovation through advanced analytics.
• As a Senior Associate, 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:

Responsibilities:

Data Engineering and Architecture:

• Design, develop, and maintain scalable and robust data pipelines, data warehouses, and data lakes.
• Collaborate with data engineers to ensure the availability, accessibility, and integrity of data.
• Optimize data storage and processing systems for efficiency and performance.
• Implement data integration strategies to consolidate and standardize data from various sources.
• Ensure data security and privacy measures are in place.

Facilitating/Training on Data and Analytics Tools and Capability:

• Conduct training sessions and workshops to enhance data literacy and analytics skills within the organization.
• Provide guidance and support to business users in utilizing data and analytics tools effectively.
• Promote self-service analytics capabilities and enable users to access and analyze data independently.
• Stay updated with the latest trends and advancements in data and analytics tools and technologies.
• Provide technical expertise and guidance to support data-related initiatives and projects.

Data Governance and Strategy:

• Develop and implement data governance frameworks and policies.
• Establish data quality standards and ensure compliance with regulatory requirements.
• Define data governance processes and procedures to ensure data accuracy, consistency, and integrity.
• Collaborate with business stakeholders to align data governance efforts with organizational objectives.
• Identify opportunities to leverage data assets and drive data-driven initiatives.

Data Science and Machine Learning:

• Apply statistical and machine learning techniques to extract insights and build predictive models.
• Develop and deploy machine learning algorithms to automate decision-making processes.
• Collaborate with data scientists to develop and optimize algorithms and models.
• Conduct exploratory data analysis and feature engineering to uncover patterns and trends.
• Evaluate and recommend appropriate tools and technologies for data science projects.

Qualifications:

• Bachelor's or Master's degree in Statistics, Computer Science, Data Science, Analytics, or a related field.
• Experience in data engineering, data architecture, data science, and machine learning.
• knowledge of data governance frameworks, data quality, and data management principles.
• Proficiency in programming languages such as Python, R, SQL, or similar languages.
• Experience with big data technologies, cloud platforms, and data processing frameworks (e.g., Hadoop, Spark, AWS, Azure) would be a plus.
• Strong analytical and problem-solving skills with the ability to translate complex data into actionable insights.
• Excellent communication and presentation skills with the ability to convey technical concepts to non-technical stakeholders.
• Proven experience in facilitating and conducting training sessions on data and analytics topics.
• Familiarity with data visualization tools and techniques (e.g., Tableau, Power BI).
• Strong leadership and project management skills, with the ability to prioritize and manage multiple initiatives.
• Continuous learning mindset to stay abreast of emerging trends and advancements in the data and analytics field.