Data Scientist

As a Data Scientist Clinical NLP & AI, you will be part of an agile team focused on building intelligent healthcare solutions by developing advanced NLP modules, integrating LLMs and agentic workflows, and leveraging AWS big data technologies to enhance clinical data processing and usability.

• Proficient developer in multiple languages, Python is a must, with the ability to quickly learn new ones.

• Expertise in SQL (complex queries, relational databases preferably PostgreSQL, and NoSQL databases - Redis and Elasticsearch).

• Extensive big data experience, including EMR, Spark, Kafka/Kinesis, and optimizing data pipelines, architectures, and datasets.

• AWS expert with hands-on experience in Lambda, Glue, Athena, Kinesis, IAM, EMR/PySpark, Docker.

• Proficient in CI/CD development using Git, Terraform, and agile methodologies.

• Comfortable with stream-processing systems (Storm, Spark-Streaming) and workflow management tools (Airflow).

• Exposure to knowledge graph technologies (Graph DB, OWL, SPARQL) is a plus.

• Experience in Machine Learning Frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost.

• Experience in model deployment - Flask, FastAPI, Docker, Kubernetes, TensorFlow Serving, TorchServe.

• Proficient developer in multiple languages, Python is a must, with the ability to quickly learn new ones.

• Expertise in SQL (complex queries, relational databases preferably PostgreSQL, and NoSQL databases - Redis and Elasticsearch).

• Extensive big data experience, including EMR, Spark, Kafka/Kinesis, and optimizing data pipelines, architectures, and datasets.

• AWS expert with hands-on experience in Lambda, Glue, Athena, Kinesis, IAM, EMR/PySpark, Docker.

• Proficient in CI/CD development using Git, Terraform, and agile methodologies.

• Comfortable with stream-processing systems (Storm, Spark-Streaming) and workflow management tools (Airflow).

• Exposure to knowledge graph technologies (Graph DB, OWL, SPARQL) is a plus.

• Experience in Machine Learning Frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost.

• Experience in model deployment - Flask, FastAPI, Docker, Kubernetes, TensorFlow Serving, TorchServe.

• Familiarity with generative AI applications in healthcare and related use cases.

• Understanding of healthcare data standards and terminologies such as HL7, FHIR, and CCDA.

• Experience in creating detailed documentation, user manuals, and technical specifications.

• Background in automated testing and validation frameworks for NLP outputs.

• Ability to collaborate effectively with cross-functional teams including engineering and products.

• Exposure to LangChain or similar frameworks for building intelligent agent workflows.