Data Scientist - W2 Role

Job Description:

-Skilled Data Scientist with deep expertise in developing, fine-tuning, and integrating AI models, particularly in natural language processing (NLP).

-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.

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

Knowledge about Agile development Methodologies.

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 of 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.