Lead Data Scientist

Job Responsibilities

• Leads and manages a team of data scientists that is innovative, collaborative, and customer-focused.
• Manages a team of data scientists, machine learning engineers, and big data specialists
• Measures business performance, develops core metrics and creates dashboards
• Spots and manages data development challenges in the organization
• Responsible for data science professional development and training in the use of modern data science tools.
• Performs deep analyses and builds models to understand customer behavior, and extract key insights that impact product decisions.
• Performs research and builds a core understanding of the company performance metrics to qualitatively inform and interpret models.
• Standardizes methods and algorithms used across the business unit.
• Develops and maintains standard software libraries and associated documentation

Requirements

• 5+ years of experience working in the data science field, preferably in a product development environment, with a focus on building NLP/LLMs or Deep Learning models.
• 2+ years of experience in managing data science teams as a lead or a mentor
• Strong programming skills in Python or other relevant programming languages.
• Experience with machine learning libraries (e.g., sci-kit-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.