Senior Data Scientist - NLP

4 days ago

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CLARA Analytics

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workers'​ compensation • occupational injuries • insurtech • data science • predictive analytics

51 - 200 employees

Founded 2016

🤖 Artificial Intelligence

☁️ SaaS

Description

• We are seeking a highly motivated and skilled Senior Data Scientist to contribute to the design, development, and deployment of NLP-based systems and machine learning models for cost analysis and predictions. • Responsibilities: Data Analysis, NLP Model Development, Collaborative Problem-Solving, Code and Model Quality, Documentation and Reporting, Compliance and Ethics, Communication, Education and Evangelism.

Requirements

• MS degree in a quantitative discipline (e.g., artificial intelligence, statistics, operations research, economics, computer science, mathematics, physics, electrical engineering). • 5+ years of experience developing machine learning models, including 2+ years of model deployment experience. • 3+ years of practical experience in natural language processing, plus a strong academic background. • Hands on experience with text preprocessing, named entity recognition, entity linking, and topic modeling. • Experience with natural language processing techniques and algorithms including LSTM, RNN, CNN, and embeddings such as BERT. • Ability to assess the pros and cons of different NLP methods and algorithms, break problems down into standard tasks and prototype quickly. • Experience with large language models and their associated tools/platforms/frameworks (LangChain, ChatGPT, AWS Foundation Models, Llama2, etc.) for use in querying large documents and entity extraction. • Proficient in Python, with experience in libraries like PySpark, Tensorflow, Pytorch, MLFlow, and NLP packages like SpaCy and NLTK. • Demonstrated ability to communicate complex quantitative concepts effectively to audiences of varying technical proficiency. • Preferred Qualifications: PhD in quantitative discipline as described above. Experience in implementing explainable AI (XAI) techniques. Knowledge of graph-based models or reinforcement learning. Familiarity with insurance-specific regulations and data privacy standards (e.g., GDPR, HIPAA). Understanding of insurance processes, including claims handling, risk modeling, or actuarial science. Publication or participation in AI/ML competitions is a plus.

Benefits

• Competitive salary and performance-based bonuses. • Comprehensive health, dental, and vision insurance. • Opportunities for professional development, including conferences and training. • A collaborative and inclusive work environment.