Line of Service
Advisory
Industry/Sector
Not Applicable
Specialism
Data Analytics & AI
Management Level
Senior Associate
Job Description & Summary
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 datadriven decisionmaking. 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.
Handson 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 SciKitLearn 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 AI102 AWS Certified Machine Learning Specialty (MLSC01 Databricks Certified Machine Learning)
Nice to have:
Strong business acumen and ability to communicate technical solutions to nontechnical 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 USbased 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 endtoend 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 / / MCA / / M.E / /Masters Degree /MBA from reputed institute
Education (if blank degree and/or field of study not specified)
Degrees/Field of Study required:
Degrees/Field of Study preferred:
Certifications (if blank certifications not specified)
Required Skills
Optional Skills
Accepting Feedback Accepting Feedback Active Listening Algorithm Development Alteryx (Automation Platform) Analytical Thinking Analytic Research Big Data Business Data Analytics Communication Complex Data Analysis Conducting Research Creativity Customer Analysis Customer Needs Analysis Dashboard Creation Data Analysis Data Analysis Software Data Collection DataDriven Insights Data Integration Data Integrity Data Mining Data Modeling Data Pipeline 38 more
Desired Languages (If blank desired languages not specified)
Travel Requirements
0
Available for Work Visa Sponsorship
No
Government Clearance Required
No
Job Posting End Date
Required Experience:
Senior IC