Job Description

As the Senior Data Scientist , you will work as part of a broader Data Science team in collaboration with Product and Engineering to deliver solutions focused on improving quality and efficiency of care delivery in the context of hospital-level care at home. You will play a crucial role in improving healthcare delivery and patient care by leveraging data science. What you’ll do # Lead data-driven decision-making by identifying key insights from large and complex datasets, driving strategic business initiatives. # Develop and implement end-to-end machine learning pipelines, including data preprocessing, feature engineering, model training, and deployment. # Design, develop, and deploy cutting-edge NLP models for tasks such as text classification, sentiment analysis, and named entity recognition. # Lead the research and application of advanced NLP techniques, including transformers, BERT, GPT, and other deep learning frameworks. # Collaborate with product managers and engineers to translate business requirements into scalable technical solutions. # Optimize and fine-tune NLP models for performance, accuracy, and scalability in production environments. # Mentor and guide junior data scientists, promoting a culture of innovation and continuous learning. Basic qualifications # Bachelor’s degree in Computer Science, Data Science, Statistics, or a related field. #3+ years of hands-on experience in data science or machine learning, with a focus on NLP. Preferred qualifications # Master’s or Ph.D. in Computer Science, Data Science, Statistics, or a related field. # Extensive experience with large-scale NLP models and frameworks (e.g., BERT, GPT-3/4, T5). # Proficiency in fine-tuning and optimizing pre-trained language models. # Experience with transfer learning and domain adaptation for NLP tasks. # Strong programming skills in Python, with expertise in NLP libraries such as spaCy, Hugging Face, or NLTK. # Familiarity with deep learning frameworks like TensorFlow or PyTorch. # Experience deploying machine learning models on cloud platforms (e.g., AWS, GCP, Azure). # Knowledge of containerization tools like Docker and orchestration with Kubernetes. # Solid understanding of MLOps practices for continuous integration, deployment, and monitoring of ML models. # Familiarity with tools like MLflow, Kubeflow, or similar platforms. # Prior experience in leading or mentoring a team of data scientists or machine learning engineers. # Proven ability to manage multiple projects and deliver results in a fast-paced environment. # Knowledge of advanced NLP techniques such as reinforcement learning, conversational AI, or graph-based models. #J-18808-Ljbffr

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