Data Scientist (6-8 years, Early Joiner preferred)

Proposed designation: Sr. Associate 2 - Data Scientist

Role type: Individual contributor

Reporting to Associate Director

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 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 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

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

• 6+ years of experience in data science, with a focus on AI, ML, and NLP technologies.
• 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.