Data Scientist , AI Engineer-Senior Associate

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 4+ years of hands on experience

Position: Senior Associate

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

Must Have

• Strong foundation in machine learning principles and AI model development.
• Experience with deep learning frameworks such as TensorFlow and PyTorch, including model deployment pipelines.
• Skilled in designing and maintaining APIs for AI services, focusing on RESTful principles.
• Proficient in using cloud AI platforms like Vertex AI, Azure ML, and SageMaker for model deployment and management.
• Good understanding and practical application of MLOps practices, including CI/CD for ML, model monitoring, and version control.
• Experience in building and maintaining robust data infrastructure, implementing standard data models, and developing ETL/ELT systems.
• Expertise in data acquisition and ingestion pipelines, data quality testing, and implementing data access and security tools.
• Proficient in SQL and NoSQL databases, with experience in designing and optimizing database systems.
• Knowledgeable in data exchange protocols like REST and JDBC, and experienced with Apache Spark for big data processing.
• Hands-on experience in designing and deploying AI pipelines using ML engineering tools such as MLflow, DVC, Kubeflow, and Airflow.
• Strong programming skills in Python, PySpark, and SQL, with an understanding of software engineering practices.
• Proficiency in data visualization tools such as Tableau, Power BI, Looker, or Streamlit for creating insightful visualizations.
• Experience with DWH software engineering, including GitHub, CI/CD, and code testing and analysis.
• Skilled in using AI/ML frameworks such as TensorFlow, PyTorch, and SciKit-Learn for developing machine learning models.
• Experience with cloud infrastructure services like Azure and GCP, and containerization technologies such as Docker and Kubernetes.
• Familiarity with observability and monitoring tools like Prometheus and ELK stack, adhering to SRE principles and techniques.
• Knowledgeable in integrating DevOps, MLOps, and DataOps practices to enhance operational efficiency and model deployment.
• Solid understanding of foundational data science concepts, including statistics, linear algebra, and machine learning principles.
• 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

• Strong business acumen and ability to communicate technical solutions to non-technical stakeholders
  
  

Roles And Responsibilities

• Collaborate with engineering teams and executive leadership on functional and process design, scenario mapping, prototyping, testing, and training.
• Document and articulate solutions architecture, capturing lessons learned during exploration and incubation of AI technologies.
• Manage teams conducting assessments of AI and automation markets, analyzing competitor strategies and technological advancements.
• Serve as a liaison between stakeholders and project teams, facilitating feedback loops to enhance product performance and presentation.
• Develop and execute project plans, interacting with US-based consultants/clients to formalize data sources, acquire datasets, and clarify use cases.
• Conduct analysis using advanced tools, implement quality control measures, and coach junior team members to ensure deliverable integrity.
• Design and maintain end-to-end ML pipelines and APIs in cloud environments, deploying models using MLOps tools like Vertex AI, SageMaker, or Azure ML.
• Collaborate with data engineers and developers for seamless integration, implementing CI/CD pipelines using GitHub Actions, Azure DevOps, or Cloud Build.
• Validate analysis outcomes with stakeholders, build storylines for presentations, and effectively communicate results and recommendations to both technical and business audiences.
  
  

Professional And Educational Background

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