Job
Description
Role Overview: At PwC, your role in data and analytics engineering will involve leveraging advanced technologies and techniques to design and develop robust data solutions for clients. You will play a crucial part in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. As a professional in data science and machine learning engineering at PwC, you will focus on utilizing advanced analytics and machine learning techniques to extract insights from large datasets, facilitating data-driven decision-making. Your responsibilities will include developing predictive models, conducting statistical analysis, and creating data visualisations to solve complex business problems. Key Responsibilities: - Design, implement, and maintain end-to-end Machine Learning Operations (MLOps) pipelines for automated model generation, training, and deployment using automated machine learning (AutoML) tools. - Manage and optimize the MLOps infrastructure on cloud platforms (e.g., AWS, Azure, GCP), including compute resources, containerization (Docker), and orchestration (Kubernetes). - Continuously monitor the performance, accuracy, and reliability of deployed models, implementing automated alerts to detect model drift or data quality issues. - Collaborate with data scientists and business teams to translate complex model results and insights into intuitive, interactive dashboards and visual reports. - Design and build data visualizations using BI tools (e.g., Tableau, Power BI) and programming libraries (e.g., D3.js, Python with Matplotlib/Seaborn) to effectively communicate model performance and business impact. - Serve as a liaison between data science, engineering, and business teams, gathering requirements and providing technical guidance to improve decision-making through visuals. - Implement best practices for version control of models, code, and data. Maintain clear documentation of pipelines, models, and visualizations for reproducibility and governance. - Diagnose and resolve issues related to ML model deployment, performance, and data pipelines. Qualifications Required: - Proven experience in both MLOps practices and designing effective data visualizations. - Hands-on experience with automated and MLOps platforms such as Vertex AI, Azure ML, or MLflow. - Proficiency in Python is required. Experience with JavaScript (D3.js, React) is a plus for advanced visualization. - Expertise with business intelligence tools like Tableau, Power BI, or Looker. - Proficiency in SQL and experience with both relational and NoSQL databases. - Hands-on experience with Docker and Kubernetes. - Experience with CI/CD tools for automating workflows. - Strong statistical knowledge and the ability to interpret model outputs and complex datasets. - A strong understanding of visual design principles, user experience (UX), and storytelling with data. - Exceptional verbal and written communication skills to convey complex technical information to both technical and non-technical audiences. Education: - Any Graduate/ A master's degree or PhD in Computer Science, Data Science, Statistics, or a related quantitative field is preferred.,
