Data Scientist/ Machine Learning Engineer

Job description Position Overview:

What you will do:

• Understand business and product needs and use classical ML methods or advanced AI techniques to solve them at scale
• Design, train, and fine-tune machine learning models for information extraction, and evaluate model performance using relevant metrics and iteratively improve the models.
• Communicate and collaborate with engineering/cross-functional teams to implement a feedback mechanism to optimize the models by training, tuning and evaluating them on a timely basis.

What you will need:

• Bachelors/Masters Degree or equivalent, with 5-8 years of experience in solving industry problems using statistical, traditional ML and AI methods with proven experience in developing machine learning or NLP models, particularly for information extraction tasks
• Deep understanding, both theory and practice, of basic statistical methods such as regression, clustering, general ML algorithms such as SVM, tree-based methods, neural networks etc. for solving supervised and unsupervised problems.
• Experience working on advanced NLP methods like feature extraction, tagging and entity recognition and classification to identify sensitive information that comply with relevant data privacy regulations from structured and unstructured data sources
• Understanding of data privacy regulations and best practices.
• Good Understanding of evaluation metrics specific to NLP tasks
• Skills in working with LLMs through prompt engineering, fine-tuning pre-trained models on specific datasets for targeted applications, etc.
• Work closely with data engineers, software developers, and other stakeholders to integrate the solutions into existing systems with systemic feedback and continuous training and optimization.

Technical Skills:

• Proficiency in programming languages such as Python
• Experience with machine learning frameworks (e.g., Sklearn, TensorFlow, PyTorch)
• Experience with NLP libraries and frameworks (e.g., spaCy, NLTK, Hugging Face Transformers)
• Familiarity with libraries and frameworks commonly used for LLMs (e.g., Hugging Face Transformers, LlamaIndex, LangChain etc)

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