4 - 7 years
7 - 8 Lacs
Posted:3 hours ago|
Platform:
Work from Office
Full Time
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 engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. In data engineering at PwC, you will focus on designing and building data infrastructure and systems to enable efficient data processing and analysis. You will be responsible for developing and implementing data pipelines, data integration, and data transformation solutions.
Job Description & Summary: A career within .
Responsibilities:
Develop a deep understanding of different types of data, metrics, and KPIs to identify and formulate data science problems that drive impactful solutions. Proficiency in Python/PySpark for AI development, data preprocessing, and scripting. Strong understanding of core machine learning and statistical concepts including supervised, unsupervised, and reinforcement learning, feature engineering, and model optimization. Hands-on experience in building, training, and deploying ML models for use cases such as prediction, recommendation, NLP, and computer vision. Proficiency in Python and machine learning libraries such as scikit-learn, TensorFlow, PyTorch, XGBoost, and Hugging Face Transformers. Familiarity with deep learning architectures CNNs, RNNs, Transformers and use cases in NLP, CV, and recommendation systems. Experience with data preprocessing, feature selection, model evaluation, and hyperparameter tuning techniques. Ability to design and deploy scalable ML pipelines leveraging cloud platforms preferably AWS, but Azure experience is also acceptable. Familiarity with API development, microservices, and integrating ML models into enterprise applications.
Understanding of data security, governance, and cost optimization in cloud-based AI environments. Experience in AWS AI and ML services, including SageMaker, Lambda, Step Functions, API Gateway, and CloudWatch, for model deployment and orchestration. Ability to work with containerization tools (Docker, Kubernetes) for deploying ML workloads.
Mandatory skill sets:
ML & Azure/AWS
Preferred skill sets:
MLOps frameworks (MLflow, Kubeflow, or Vertex AI Pipelines).
Years of experience required:
4 - 7 yrs
Education qualification:
Btech/MBA/MCA
Education (if blank, degree and/or field of study not specified)
Degrees/Field of Study required: Bachelor of Engineering, Master of Engineering
Degrees/Field of Study preferred:
Certifications (if blank, certifications not specified)
Required Skills
Machine Learning (ML)
Optional Skills
Accepting Feedback, Accepting Feedback, Active Listening, Agile Scalability, Amazon Web Services (AWS), Analytical Thinking, Apache Airflow, Apache Hadoop, Azure Data Factory, Communication, Creativity, Data Anonymization, Data Architecture, Database Administration, Database Management System (DBMS), Database Optimization, Database Security Best Practices, Databricks Unified Data Analytics Platform, Data Engineering, Data Engineering Platforms, Data Infrastructure, Data Integration, Data Lake, Data Modeling, Data Pipeline {+ 27 more}
Desired Languages (If blank, desired languages not specified)
Travel Requirements
Not Specified
Available for Work Visa Sponsorship
No
Government Clearance Required
No
Job Posting End Date
PwC Service Delivery Center
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