Data Science Engineering Manager needed at PricewaterhouseCoopers

Key Responsibilities: 

• Design and implement robust data pipelines to collect, clean, and process large volumes of structured and unstructured data. 
• Optimize data workflows and machine learning systems to ensure efficiency and reliability. 
• Work closely with data analysts, data scientists, and stakeholders to define data requirements and deliver actionable insights. 
• Contribute on AI projects using RAG and LLM. 
• Perform exploratory data analysis and contribute to the creation of predictive and prescriptive analytics. 
• Stay updated with the latest trends in data science, machine learning, and big data technologies. 

Qualifications: 

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field. 
• Proficiency in programming languages such as Python, R, or Scala. 
• Strong experience with big data tools and frameworks (e.g., Spark, Databricks) and cloud platforms (e.g., AWS, Azure, Google Cloud). 
• Solid understanding of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn). 
• Hands-on experience with SQL and NoSQL databases. 
• Knowledge of CI/CD pipelines and MLOps principles. 
• Excellent problem-solving skills and a keen eye for detail. 
• Strong communication skills and the ability to work collaboratively in a team environment. 

Preferred Skills: 

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