Data Scientist (Hybrid as needed)

Northwell Health

22h•Hybrid

About The Position

You will plan, implement, deploy, maintain/evaluate data-driven solutions that will directly impact patients and healthcare delivery; communicate with adjacent engineering teams as well as non-technical/clinical stakeholders to understand how their needs can be translated into data science solutions. You will validate models used in the healthcare setting to ensure that they are improving patient care and safety. Ensures that these projects and their outputs enhance clinical quality, patient safety, and institutional efficiency, focusing on all aspects of data science, including data gathering and wrangling, exploratory data analysis, data modeling and machine learning, and model implementation and evaluation. Provides expertise on mathematical concepts for the broader applied analytics team. Involvement throughout the end-to-end lifecycle of complex data science projects/programs including problem scoping, due diligence, evaluation/validation/model design and development, deployment to production systems, monitoring prediction output, and measuring value Have strong communication skills to act as the bridge between technical and non-technical team members and stake holders Support the data science team in technical and strategic analyses for decision making related to machine learning/AI Enthusiasm for self-directed learning Works collaboratively to design, implement and maintain data science models and applications. Works on programs to apply data science methodologies, including predictive modeling and machine learning, data analytics and visualization, and usability and design, to departmental, service line, and enterprise applications and functions. Synthesizes complex data-related problems into actionable business and/or clinical strategy, and communicate findings to appropriate end-users and stakeholders. Assists with the development of specifications to support the design of new or modified data science projects, with a focus on data-driven optimization, enhancement, and development. Assists in the evaluation of projects, systems, and initiatives at the department, service line, and enterprise level; ensures projects and their outputs enhance clinical quality, patient safety, and institutional efficiency, focusing on all aspects of data science, including data gathering and wrangling, exploratory data analysis, data modeling and machine learning, and model implementation and evaluation; ensures high quality execution of all proposed projects. Knowledgeable in present and planned data science projects and maintains voice of the customer in all project initiatives. Serves as the link between the clinical staff (customer) requirements and IS capabilities. Assists in ensuring that systems are implemented to support organization initiatives and goals to improve the quality of patient care, to maximize patient safety, and to provide operational efficiencies. Serves as a resource to the leadership; demonstrates familiarity with current hospital information systems. Operates under general guidance and work assignments are varied and require interpretation and independent decisions on course of action. Performs related duties as required. All responsibilities noted here are considered essential functions of the job under the Americans with Disabilities Act. Duties not mentioned here, but considered related are not essential functions.

Requirements

Bachelor’s Degree in Computer Science, Informatics, Statistics, Engineering, Data Science, or related field, required.
Minimum of two (2) years of post-graduate training or experience involving quantitative data analysis, required
Working familiarity with basic medical and health information technology concepts, including standardized terminologies and ontologies and electronic health records, as well as Data Warehousing and Business Intelligence tools, required.
Expertise in working with SQL relational databases and statistical or general programming languages (e.g., Python, R), required.
Deep understanding of statistical and predictive modeling concepts, machine-learning approaches, clustering and classification techniques, and recommendation and optimization algorithms.

Nice To Haves

Master’s Degree, preferred.
Experience working with clinical data, data science, and machine learning, preferred.
Clear and effective communication and presentation skills are highly preferred
Comfortable presenting to non-technical/clinical stakeholders and executive leadership
Experience in data science projects with organizational-level impact (healthcare related preferred)
Experience with cloud computing (GCP, Vertex AI preferred)
Experience with orchestration/ETL pipelines (e.g. Airflow, Prefect, Luigi, Kubeflow) with preference for Airflow
Experience with DevOps/MLOps (e.g. containerization, CI/CD, feature stores, data lineage)
Strong/advanced level proficiency in statistical analysis, machine learning techniques, model evaluation
Strong/advanced level proficiency in modern machine learning frameworks (e.g. scikit-learn), deep learning frameworks (e.g. Pytorch)
Strong/advanced level experience in training/deploying embedding models, fine-tuning large language models (various tasks), retrieval-augmented generation, prompt and context engineering, and evaluation of generative AI systems

Responsibilities

Plan, implement, deploy, maintain/evaluate data-driven solutions that will directly impact patients and healthcare delivery
Communicate with adjacent engineering teams as well as non-technical/clinical stakeholders to understand how their needs can be translated into data science solutions
Validate models used in the healthcare setting to ensure that they are improving patient care and safety
Ensure projects and their outputs enhance clinical quality, patient safety, and institutional efficiency
Provide expertise on mathematical concepts for the broader applied analytics team
Involvement throughout the end-to-end lifecycle of complex data science projects/programs including problem scoping, due diligence, evaluation/validation/model design and development, deployment to production systems, monitoring prediction output, and measuring value
Act as the bridge between technical and non-technical team members and stake holders
Support the data science team in technical and strategic analyses for decision making related to machine learning/AI
Design, implement and maintain data science models and applications
Apply data science methodologies, including predictive modeling and machine learning, data analytics and visualization, and usability and design, to departmental, service line, and enterprise applications and functions
Synthesize complex data-related problems into actionable business and/or clinical strategy, and communicate findings to appropriate end-users and stakeholders
Assist with the development of specifications to support the design of new or modified data science projects
Assist in the evaluation of projects, systems, and initiatives at the department, service line, and enterprise level
Serve as the link between the clinical staff (customer) requirements and IS capabilities
Assist in ensuring that systems are implemented to support organization initiatives and goals to improve the quality of patient care, to maximize patient safety, and to provide operational efficiencies
Serve as a resource to the leadership; demonstrates familiarity with current hospital information systems