Data Science Engineering Manager

Summary: At PwC, our risk and compliance teams focus on maintaining regulatory compliance and managing risks for clients by providing advice and solutions. They assist organizations in navigating complex regulatory environments and strengthening internal controls to mitigate risks effectively.

In actuarial services at PwC, you will analyze and manage financial risks for clients through statistical modeling and data analysis. Your work will generate insights and recommendations to help businesses make informed decisions and mitigate potential risks.

Enhancing your leadership skills, you motivate, develop, and inspire others to deliver quality. You will coach team members, leverage their strengths, and manage performance to meet client expectations. With growing business knowledge, you will identify opportunities that contribute to our Firm's success. You are expected to lead with integrity and authenticity, articulating our purpose and values meaningfully. Embracing technology and innovation, you will enhance your delivery and encourage others to do the same.

Examples of skills, knowledge, and experiences needed to lead and deliver value include but are not limited to:

1. Analyze and identify linkages and interactions within complex systems.
2. Take ownership of projects, ensuring successful planning, budgeting, execution, and completion.
3. Partner with team leadership to ensure collective ownership of quality, timelines, and deliverables.
4. Develop skills outside your comfort zone and encourage others to do the same.
5. Mentor others effectively.
6. Use work reviews to deepen team members' expertise.
7. Address conflicts or issues, engaging in difficult conversations with clients, team members, and stakeholders, escalating when appropriate.
8. Uphold professional and technical standards, including PwC tax and audit guidance, the Firm's code of conduct, and independence requirements.

Job Summary:

We seek a skilled Data Science Engineering Manager to join our team. You will work at the intersection of data engineering and data science, designing scalable systems for data collection, analysis, and machine learning deployment. Collaboration with cross-functional teams will be essential to unlock insights and develop data-driven solutions for our clients.

Key Responsibilities:

1. Design and implement robust data pipelines for collecting, cleaning, and processing large volumes of data.
2. Optimize data workflows and machine learning systems for efficiency and reliability.
3. Collaborate with data analysts, scientists, and stakeholders to define data requirements and deliver insights.
4. Contribute to AI projects using RAG and LLM techniques.
5. Perform exploratory data analysis and develop predictive and prescriptive analytics.
6. Stay updated with trends in data science, machine learning, and big data technologies.

Qualifications:

1. Bachelor's or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or related fields.
2. Proficiency in Python, R, or Scala.
3. Experience with big data tools (e.g., Spark, Databricks) and cloud platforms (AWS, Azure, Google Cloud).
4. Knowledge of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn).
5. Experience with SQL and NoSQL databases.
6. Understanding of CI/CD pipelines and MLOps principles.
7. Excellent problem-solving skills and attention to detail.
8. Strong communication skills and team collaboration ability.

Preferred Skills:

1. Knowledge of advanced analytics techniques (e.g., NLP, computer vision).
2. Experience with software development practices and version control (e.g., Git).
3. Familiarity with data visualization tools (e.g., Power BI).