Expert Data Scientist

Job Description

Role & responsibilities Strong knowledge of Probability Theory, Statistics, and a deep understanding of the Mathematics behind Machine Learning Proficiency with CRISP-ML(Q) or TDSP methodologies for addressing commercial problems through data science solutions Proficiency in Python for developing machine learning models and conducting statistical analyses Strong understanding of data visualization tools and techniques (e.g., Python libraries such as Matplotlib, Seaborn, Plotly, etc.) and the ability to present data effectively Specific technical requirements: Proficiency in SQL for data processing, data manipulation, sampling, and reporting Experience working with imbalanced datasets and applying appropriate techniques Experience with time series data, including preprocessing, feature engineering, and forecasting Experience with outlier detection and anomaly detection Experience working with various data types: text, image, and video data Familiarity with AI/ML cloud implementations (AWS, Azure, GCP) and cloud-based AI/ML services (e.g., Amazon SageMaker, Azure ML) Domain experience: Experience with analyzing medical signals and images Expertise in building predictive models for patient outcomes, disease progression, readmissions, and population health risks Experience in extracting insights from clinical notes, medical literature, and patient-reported data using NLP and text mining techniques Familiarity with survival or time-to-event analysis Expertise in designing and analyzing data from clinical trials or research studies Experience in identifying causal relationships between treatments and outcomes, such as propensity score matching or instrumental variable techniques Understanding of healthcare regulations and standards like HIPAA, GDPR (for healthcare data), and FDA regulations for medical devices and AI in healthcare Expertise in handling sensitive healthcare data in a secure, compliant way, understanding the complexities of patient consent, de-identification, and data sharing Familiarity with decentralized data models such as federated learning to build models without transferring patient data across institutions Knowledge of interoperability standards such as HL7, SNOMED, FHIR, or DICOM Ability to work with clinicians, researchers, health administrators, and policy makers to understand problems and translate data into actionable healthcare insights Preferred candidate profile Experience with MLOps, including integration of machine learning pipelines into production environments, Docker, and containerization/orchestration (e.g., Kubernetes) Experience in deep learning development using TensorFlow or PyTorch libraries Experience with Large Language Models (LLMs) and Generative AI applications Advanced SQL proficiency, with experience in MS SQL Server or PostgreSQL Familiarity with platforms like Databricks and Snowflake for data engineering and analytics Experience working with Big Data technologies (e.g., Hadoop, Apache Spark) Familiarity with NoSQL databases (e.g., columnar or graph databases like Cassandra, Neo4j)