Job
Description
In this role at PwC, you will focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. Your responsibilities will include transforming raw data into actionable insights, developing predictive models, conducting statistical analysis, and creating data visualizations to solve complex business problems. As part of the data and analytics engineering team, you will play a crucial role in enabling informed decision-making and driving business growth through data science and machine learning engineering. 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. Skills and Qualifications: - Proven experience in both MLOps practices and designing effective data visualizations. - Experience with automated and MLOps platforms such as Vertex AI, Azure ML, or MLflow. - Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP). - Strong 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 masters degree or PhD in Computer Science, Data Science, Statistics, or a related quantitative field is preferred.,
