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

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 KPMGs data governance policies and industry regulations.

Implement best practices for data privacy, security, and ethical

Educational qualifications

Bachelors or Masters 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.