Data Scientist , AI Engineer-Manager

At PwC, our people in data and analytics focus on leveraging data to drive insights and make informed business decisions. They utilise advanced analytics techniques to help clients optimise their operations and achieve their strategic goals. In data analysis at PwC, you will focus on utilising advanced analytical techniques to extract insights from large datasets and drive data-driven decision-making. You will leverage skills in data manipulation, visualisation, and statistical modelling to support clients in solving complex business problems.

Years of Experience: Candidates with 8+ years of hands on experience

Position: Manager

Required Skills: Successful candidates will have demonstrated the following skills and characteristics:

Must Have

• Strong expertise in machine learning, deep learning, and advanced statistical modeling.
• Experience delivering AI-driven solutions in areas such as forecasting, recommendation systems, NLP, computer vision, and optimization.
• Strong programming skills in Python, PySpark, and SQL, with a solid understanding of software engineering practices.
• Practical knowledge of the AI model lifecycle, from data preparation to deployment and monitoring.
• Proficient with ML frameworks like TensorFlow, PyTorch, scikit-learn, XGBoost, and H2O.
• Hands-on experience in designing and deploying AI pipelines using ML engineering tools such as MLflow, DVC, Kubeflow, and Airflow.
• Experience in REST API development, NoSQL database design, and RDBMS design and optimizations.
• Ability to work with big data platforms and tools like Apache Spark, Hive, and Delta Lake for scalable model development.
• Proficiency in data visualization tools such as Tableau, Power BI, Looker, and Streamlit.
• Programming skills in Python and either Scala or R, with experience using Flask and FastAPI.
• Experience with software engineering practices, including the use of GitHub, CI/CD, code testing, and analysis.
• Skilled in using Apache Spark, including PySpark and Databricks, for big data processing.
• Strong understanding of foundational data science concepts, including statistics, linear algebra, and machine learning principles.
• Knowledgeable in integrating DevOps, MLOps, and DataOps practices to enhance operational efficiency and model deployment.
• Experience with cloud infrastructure services like Azure and GCP.
• Proficiency in containerization technologies such as Docker and Kubernetes.
• Familiarity with observability and monitoring tools like Prometheus and the ELK stack, adhering to SRE principles and techniques.
• Cloud or Data Engineering certifications or specialization certifications (e.g. Google Professional Machine Learning Engineer, Microsoft Certified: Azure AI Engineer Associate – Exam AI-102, AWS Certified Machine Learning – Specialty (MLS-C01), Databricks Certified Machine Learning)
  
  

Nice To Have

• Experience in deploying models to production using CI/CD pipelines and MLOps practices
• Exposure to cloud platforms like Azure, AWS, or GCP, including AI/ML services (e.g., Vertex AI, SageMaker, Azure ML)
• Familiarity with API development for AI applications and containerization tools (Docker, Kubernetes)
• Strong business acumen and ability to communicate technical solutions to non-technical stakeholders
  
  

Roles And Responsibilities

• Lead and manage AI and data science projects from design to deployment, ensuring high-quality and scalable delivery.
• Translate complex business problems into AI-driven solutions, leveraging predictive modeling, optimization, or generative AI.
• Architect and oversee the development of AI pipelines and data workflows, incorporating best practices for reproducibility and monitoring.
• Guide project teams on data engineering, model development, validation, and operationalization.
• Communicate technical solutions effectively to business stakeholders through presentations, dashboards, and storyboards.
• Foster strong client relationships by acting as a strategic advisor and aligning AI solutions with business goals.
• Mentor junior team members and build a culture of innovation, learning, and collaboration.
• Drive knowledge sharing, intellectual property creation, and support pre-sales activities through thought leadership and solution development.
• Partner effectively with cross-functional teams, including data scientists, data managers, analysts, and infrastructure engineers, to develop, operationalize, integrate, and scale new algorithmic products.
• Develop code, CI/CD, and MLOps pipelines, including automated tests, and deploy models to cloud compute endpoints.
• Manage cloud resources and build accelerators to enable other engineers, demonstrating practical experience across modern software and AI engineering stacks.
• Work autonomously with a successful track record of building multiple large-scale production-grade AI products for large organizations, with experience in two hyperscale clouds, and effectively communicate while coaching and leading junior engineers.
  
  

Professional And Educational Background

BE / B.Tech / MCA / M.Sc / M.E / M.Tech /Master’s Degree /MBA from reputed institute