200+ applicants

Company

Northwestern Memorial Hospital · 11 hours ago

Data Scientist

Chicago, IL

Full-time

Remote

Mid Level

3+ years exp

Maximize your interview chances

Health Care

Insider Connection @Northwestern Memorial Hospital

Discover valuable connections within the company who might provide insights and potential referrals.
Get 3x more responses when you reach out via email instead of LinkedIn.

Responsibilities

As a member of the Data Science team, you will work with stakeholders to design, build, and evaluate solutions that solve business and clinical problems within the organization.

You will use data, statistics, and machine learning tools to construct solutions that meet the requirements of the problem and deliver those solutions to make a tangible impact.

Bringing a product mindset and technical acumen, you will design solutions that deliver value and can be maintained.

We do not just build models; we collaborate with experts to understand and solve problems.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Machine LearningPythonRSQLGitHub/Azure DevOpsDeep LearningNatural Language ProcessingComputer VisionCloud PlatformsFeature EngineeringProject ManagementStakeholder ManagementSystems Level ThinkingProduct Mindset

Required

Bachelor's degree or equivalent experience in CS, Engineering, Math, Physics, or a related field

At least 3 years of experience with machine learning with a focus on model building, analysis of outcomes, and model deployment into usage.

Experience defining tasks, managing projects, and delivering on time

Strong coding skills in general purpose programming languages like Python or R.

Experience with machine learning algorithms and frameworks like scikit-learn (sklearn) or SparkML for building and evaluating models

Familiarity with software engineering principles around testing, code reviews and deployment

Strong understanding of SQL for data manipulation and extraction from databases

Strong problem-solving skills with the ability to analyze complex problems, break them down into manageable tasks, and develop effective solutions

Ability to work effectively in interdisciplinary teams, including clinicians, to understand their needs and translate them into data-driven solutions

Stakeholder management skills with experience building and growing relationships with stakeholders to ensure alignment and successful implementation of data-driven solutions

Continuous learning mindset with a willingness to say up to date with the latest advancements in data science and healthcare, and actively seek opportunities for professional growth and development

Knowledge of feature engineering and selection methods to improve model performance

Proficiency in using GitHub/Azure DevOps for version control to manage code repositories, track changes, collaborate with team members, and maintain version control for data science projects

Preferred

Master's degree or equivalent experience in CS, Engineering, Math, Physics, or a related field

Experience running projects from inception to deployment. Familiar with the lifecycle of product development

Experience with deep learning frameworks, such as PyTorch, for building and training neural networks

Knowledge of Natural Language Processing techniques and libraries, such as NLTK or spaCy, for text analysis and processing

Familiarity with Computer Vision concepts and frameworks, like OpenCV, for image analysis and object detection

Experience using cloud platforms like Azure for scalable data processing and model deployment

Domain knowledge in healthcare. Understanding of healthcare terminology, workflows, and challenges, enabling effective collaboration with clinicians and healthcare professionals

Systems level thinking. Ability to think about how a solution will be used and identify other components that need to be built or integrated

Product mindset. Able to design for solving the current problem reliably, but also so the solution can be applied to other problems. Design systems so they can be maintained