Manager

KPMG Delivery Network (KDN) is seeking an experienced Data Scientist with a strong background in Generative AI, Machine Learning (ML), Natural Language Processing (NLP), and other AI technologies to join our dynamic KDN AI Foundry team. As a Data Scientist, you will play a crucial role in developing advanced AI models and solutions that drive innovation and provide actionable insights for KPMG’s global network. This role requires a deep understanding of AI methodologies, a strong ability to work with large datasets, and the skills to translate complex business problems into AI-driven solutions.
Key responsibilities include:
1.
AI Model Development:
•
Design, develop, and implement machine learning models and algorithms that address specific business challenges, leveraging Generative AI, NLP, and other AI technologies.
•
Develop and fine-tune advanced AI models, including deep learning models, to improve performance and accuracy across various use cases.
•
Experiment with and apply state-of-the-art techniques in Generative AI (e.g., GPT, GANs) to create innovative solutions for complex problems.
2.
NLP and Text Analytics:
•
Develop NLP models for tasks such as sentiment analysis, entity recognition, text summarization, and language translation.
•
Work on text processing pipelines, including tokenization, stemming, lemmatization, and vectorization techniques.
•
Implement NLP solutions using frameworks such as SpaCy, BERT, GPT, and other transformer models.
3.
Data 3.
Data Analysis and Feature Engineering:
•
Conduct exploratory data analysis (EDA) to understand data patterns and relationships.
•
Engineer and select features that improve the performance of AI models, using domain knowledge and statistical techniques.
•
Handle large datasets, ensuring data quality, consistency, and completeness for model training and evaluation.
4.
Collaboration and Cross-Functional Work:
•
Collaborate with AI engineers, data engineers, and product managers to translate business requirements into technical solutions.
•
Work closely with stakeholders across KPMG member firms to understand their needs and ensure the AI solutions meet business objectives.
•
Participate in code reviews, share best practices, and mentor junior data scientists to foster a collaborative and high-performance environment.

Model Deployment and Optimization:
•
Deploy AI models in production environments, ensuring they are scalable, reliable, and maintainable.
•
Continuously monitor model performance, retraining and updating models as necessary to maintain accuracy and relevance.
•
Optimize models for performance, speed, and resource efficiency, particularly when working with cloud platforms such as Azure.
6.
Research and Innovation:
•
Stay up-to-date with the latest advancements in AI, ML, NLP, and related fields, applying new methodologies to enhance existing models.
•
Conduct research to identify new AI use cases and opportunities for innovation within KDN AI Foundry.
•
Publish findings, contribute to technical documentation, and present insights to stakeholders and at industry conferences.
7.
Data Governance and Security:
•
Ensure all AI models and data processing activities comply with KPMG’s data governance policies and industry regulations.
•
Implement best practices for data privacy, security, and ethical AI, particularly when handling sensitive and confidential data.
© 2024

Key responsibilities include:
1.
AI Model Development:
•
Design, develop, and implement machine learning models and algorithms that address specific business challenges, leveraging Generative AI, NLP, and other AI technologies.
•
Develop and fine-tune advanced AI models, including deep learning models, to improve performance and accuracy across various use cases.
•
Experiment with and apply state-of-the-art techniques in Generative AI (e.g., GPT, GANs) to create innovative solutions for complex problems.
2.
NLP and Text Analytics:
•
Develop NLP models for tasks such as sentiment analysis, entity recognition, text summarization, and language translation.
•
Work on text processing pipelines, including tokenization, stemming, lemmatization, and vectorization techniques.
•
Implement NLP solutions using frameworks such as SpaCy, BERT, GPT, and other transformer models.
3.
Data 3.
Data Analysis and Feature Engineering:
•
Conduct exploratory data analysis (EDA) to understand data patterns and relationships.
•
Engineer and select features that improve the performance of AI models, using domain knowledge and statistical techniques.
•
Handle large datasets, ensuring data quality, consistency, and completeness for model training and evaluation.
4.
Collaboration and Cross-Functional Work:
•
Collaborate with AI engineers, data engineers, and product managers to translate business requirements into technical solutions.
•
Work closely with stakeholders across KPMG member firms to understand their needs and ensure the AI solutions meet business objectives.
•
Participate in code reviews, share best practices, and mentor junior data scientists to foster a collaborative and high-performance environment.

Model Deployment and Optimization:
•
Deploy AI models in production environments, ensuring they are scalable, reliable, and maintainable.
•
Continuously monitor model performance, retraining and updating models as necessary to maintain accuracy and relevance.
•
Optimize models for performance, speed, and resource efficiency, particularly when working with cloud platforms such as Azure.
6.
Research and Innovation:
•
Stay up-to-date with the latest advancements in AI, ML, NLP, and related fields, applying new methodologies to enhance existing models.
•
Conduct research to identify new AI use cases and opportunities for innovation within KDN AI Foundry.
•
Publish findings, contribute to technical documentation, and present insights to stakeholders and at industry conferences.
7.
Data Governance and Security:
•
Ensure all AI models and data processing activities comply with KPMG’s data governance policies and industry regulations.
•
Implement best practices for data privacy, security, and ethical AI, particularly when handling sensitive and confidential data.
© 2024

Educational qualifications
•
Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Statistics, or a related field.
•
Advanced certifications in AI/ML, Data Science, or NLP are advantageous.
Work experience
•
7+ years of experience in data science, with a focus on AI, ML, and NLP technologies.
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Proven track record of developing and deploying AI/ML models in production environments.
•
Hands-on experience with AI frameworks and libraries such as TensorFlow, PyTorch, Keras, and transformer models like BERT and GPT.
•
Experience working with large datasets, data preprocessing, and feature engineering.
Skills

Strong proficiency in programming languages such as Python, R, or Scala, with a focus on AI/ML libraries.
•
Deep understanding of machine learning algorithms, statistical methods, and NLP techniques.
•
Familiarity with cloud platforms (e.g., Azure, AWS) and big data technologies (e.g., Hadoop, Spark).
•
Excellent problem-solving skills and the ability to work independently and as part of a team.
•
Strong communication skills, with the ability to present complex technical concepts to non-technical stakeholders.