Data Scientist

Data Scientist

Location: Remote

We are seeking a skilled Data Scientist with deep expertise in developing, fine-tuning, and integrating AI models, particularly in natural language processing (NLP). This role will focus heavily on analyzing unstructured medical records, developing AI models for extracting insights, and incorporating human-in-the-loop feedback to improve model performance. You will work closely with software engineers and other stakeholders to ensure that AI solutions are effectively integrated into the overall system architecture.

Required Qualifications & Experience:

• 5+ years of experience in AI/ML development with a strong focus on NLP using frameworks such as TensorFlow, PyTorch, and Hugging Face
• Expertise in Python, with experience in libraries like Transformers, NLTK, SpaCy, Gensim, and data manipulation tools such as Pandas and NumPy
• Experience working with human-in-the-loop systems, integrating clinician feedback to refine AI models
• Ability to effectively articulate technical challenges and solutions
• Strong communicator with excellent written and verbal communication skills
• Knowledge about Agile development Methodologies.
• Identify and analyze user requirements to generate stories and tasks for team backlog
• Prioritize and execute tasks throughout the software development life cycle
• Create custom NLP algorithms and annotators to evaluate medical record data
• Create custom tools to enable analysts to perform data research
• Solid understanding of statistical modeling, data analysis, and performance evaluation metrics.
• Demonstrated experience analyzing and processing unstructured clinical data (e.g., electronic health records, physician notes, imaging reports), using techniques such as tokenization, lemmatization, and word embeddings (e.g., TF-IDF, BERT)
• Familiarity with healthcare data formats and standards such as HL7, FHIR, ICD codes, and SNOMED
• Experience with cloud platforms (AWS, Azure), containerization (Docker), and using CI/CD pipelines for machine learning model deployment
• Knowledge of SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Elasticsearch) databases, and how to structure data pipelines for efficient data processing
• Develop and fine-tune AI models for natural language processing (NLP) tasks, including Named Entity Recognition (NER), text classification, and sentiment analysis, particularly with unstructured clinical records
• Conduct experiments to evaluate model performance, utilizing metrics such as precision, recall, and F1-score to iteratively improve models through hyperparameter tuning and training optimizations
• Analyze and preprocess large datasets, particularly unstructured medical records (e.g., physician notes, discharge summaries), using tools like Pandas, NLTK, and SpaCy
• Master s degree (Data Science, AI, Computer Science, or a related field) + 10 years experience; or PhD + 4 years
• Preferred Qualifications:
• Experience in healthcare, particularly working with unstructured medical records in clinical settings, leveraging NLP models for insight extraction.
• Experience working with human-in-the-loop systems, incorporating clinician/end-user feedback and leveraging tools like SciPy and NumPy to improve AI model accuracy
• Educational background or practical training in a clinical setting, with exposure to clinical workflows and medical terminologies
• Familiarity with deep learning techniques, attention mechanisms, and transformers applied to healthcare data