Data Scientist , AI Engineer- Senior 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 12+ years of hands on experience

Position: Senior Manager

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

Must Have

• Deep expertise in AI/ML solution design, including supervised and unsupervised learning, deep learning, NLP, and optimization.
• Strong hands-on experience with ML/DL frameworks like TensorFlow, PyTorch, scikit-learn, H2O, and XGBoost.
• Solid programming skills in Python, PySpark, and SQL, with a strong foundation in software engineering principles.
• Proven track record of building end-to-end AI pipelines, including data ingestion, model training, testing, and production deployment.
• Experience with MLOps tools such as MLflow, Airflow, DVC, and Kubeflow for model tracking, versioning, and monitoring.
• Understanding of big data technologies like Apache Spark, Hive, and Delta Lake for scalable model development.
• Expertise in AI solution deployment across cloud platforms like GCP, AWS, and Azure using services like Vertex AI, SageMaker, and Azure ML.
• Experience in REST API development, NoSQL database design, and RDBMS design and optimizations.
• Familiarity with API-based AI integration and containerization technologies like Docker and Kubernetes.
• Proficiency in data storytelling and 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 use of GitHub, CI/CD, code testing, and analysis.
• Proficient in using AI/ML frameworks such as TensorFlow, PyTorch, and SciKit-Learn.
• 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 implementing generative AI, LLMs, or advanced NLP use cases
• Exposure to real-time AI systems, edge deployment, or federated learning
• Strong executive presence and experience communicating with senior leadership or CXO-level clients
  
  

Roles And Responsibilities

• Lead and oversee complex AI/ML programs, ensuring alignment with business strategy and delivering measurable outcomes.
• Serve as a strategic advisor to clients on AI adoption, architecture decisions, and responsible AI practices.
• Design and review scalable AI architectures, ensuring performance, security, and compliance.
• Supervise the development of machine learning pipelines, enabling model training, retraining, monitoring, and automation.
• Present technical solutions and business value to executive stakeholders through impactful storytelling and data visualization.
• Build, mentor, and lead high-performing teams of data scientists, ML engineers, and analysts.
• Drive innovation and capability development in areas such as generative AI, optimization, and real-time analytics.
• Contribute to business development efforts, including proposal creation, thought leadership, and client engagements.
• Partner effectively with cross-functional teams 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, with experience in working across two hyperscale clouds.
• Demonstrate effective communication skills, coaching and leading junior engineers, with a successful track record of building production-grade AI products for large organizations.
  
  

Professional And Educational Background

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