Middle Data Scientist with NLP/LLM expertise (Data Competency Center)

Requirements

• Profound expertise in NLP and familiarity with LLMs, backed by solid experience in Python
• Demonstrated ability to deploy NLP models to address real-world challenges, with a strong foundation in classical ML techniques and various data sources
• Proficiency in data visualization tools (e.g., Matplotlib, Seaborn, Tableau, Power BI) to articulate model outcomes and insights effectively
• A robust portfolio of projects showcasing the application of NLP and classical ML techniques to derive actionable insights from diverse data sources
• Continuous learner, keen on staying updated with the latest in NLP, and LLM advancements
• English proficiency: Confident Intermediate level or higher (both written and spoken)

WOULD BE A PLUS:

• AWS/Azure general understanding and experience
• Upper-Intermediate level of English

Personal Profile

• Demonstrated problem-solving and analytical thinking skills, with a proven track record of applying these skills to real-world challenges to identify problems, gather relevant data, and develop creative solutions
• Continuous learning mindset, ensuring you stay updated with the latest advancements in deep learning and adapt skills accordingly

Responsibilities

• Collaborate closely with clients and Team Leads to precisely define technical requirements and expectations, particularly focusing on NLP and LLM technologies
• Lead the charge in translating complex business issues into clear, actionable NLP and LLM hypotheses and solutions
• Develop and implement cutting-edge NLP techniques and LLM frameworks (e.g., BERT, GPT, RAG) to analyze, generate, and interpret extensive natural language datasets
• Integrate classical machine learning methodologies, including statistical analysis, clustering, classification, and recommendation engines, into NLP solutions to enhance model performance and insights
• Manage the full lifecycle of NLP and LLM solutions, from data collection and preprocessing to model training, deployment, and maintenance
• Ensure seamless deployment of NLP and LLM models into production environments, maintaining scalability and performance
• Communicate complex NLP and LLM insights and model outcomes to stakeholders, effectively bridging technical solutions with business strategies
• Actively participate in the evaluation of new tools for analytical data engineering or data science