Data Scientist - II

Job Responsibilities

• Design, develop and deploy machine learning models and algorithms for NLP/LLMs or deep learning models at scale to solve complex business problems.
• Work with large datasets, perform data analysis and develop data pipelines to support model development.
• Collaborate with Product & Engineering teams to integrate machine learning solutions into products and services.
• Conduct experiments, test hypotheses, and perform statistical analysis to validate models and drive improvements.
• Communicate findings and insights to stakeholders in a clear and concise manner.
• Stay up-to-date with the latest developments in machine learning, NLP/LLMs, deep learning, and related technologies.
• Provide technical mentorship and guidance to junior team members.

Requirements

• 3+ years of experience as a data scientist, preferably in a product development environment, with a focus on building NLP/LLMs or Deep Learning models.
• Strong programming skills in Python or other relevant programming languages.
• Experience with machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Deep understanding of statistical analysis, probability theory, and experimental design.
• Experience with LLMs, deep learning, NLP and chatbot development.
• Excellent communication skills, including the ability to explain complex concepts to technical and non-technical stakeholders.
• Experience working with a variety of statistical models, including logistic regression, clustering, classification, SVMs, neural networks, Random Forest, CRF, Bayesian models, supervised/unsupervised learning, etc.
• Expertise in NLP techniques, including sentiment analysis, word embedding, part-of-speech (POS) tagging, topic modeling, text classification, machine translation, speech recognition, named entity recognition (NER), natural language generation (NLG), and other related techniques.
• Experience with various deep learning techniques, including CNNs and RNNs, and a strong understanding of building and training these models for different applications. Familiarity with LMs and LLMs such as GPT, BERT, and Transformer models is highly desirable.
• Strong ability to rapidly comprehend and implement research papers related to AI, as well as remain informed of the latest advancements in NLP technologies.
• Deep knowledge and experience in structured and unstructured data Information Extraction, Knowledge Information Retrieval, and Knowledge Representation.
• Self-motivated and driven to satisfy intellectual curiosity through the pursuit of continuous learning and skill development.
• Strong problem-solving and analytical skills.
• Optional: Experience with large-scale data processing technologies (e.g., Hadoop, Spark) and distributed computing systems.