We are seeking a skilled and analytical Data Scientist to join our team. The ideal candidate will have strong expertise in data science fundamentals, statistical inference, and machine learning approaches applicable to donor behavior modeling. This role will involve designing, implementing, and optimizing machine learning models to derive insights and support data-driven decision-making.

As a Data Scientist, you will be responsible for developing and improving predictive models that identify relationships between patient experiences, gratitude indicators, and philanthropic potential. We are looking for an experienced problem solver with an ability to understand customer needs, support and maintain ML models in a data warehouse, and someone who willingly collaborates on related issues and innovative enhancements.

This position requires an understanding of feature engineering, model validation, and the ability to create interpretable solutions. As our Data Scientist, you’ll need to possess an understanding of the statistical foundations of various analytical techniques and be able to select and implement the appropriate methodology to tackle specific challenges in healthcare philanthropic giving.

• Develop predictive models to identify high-potential donors by analyzing patient experience data and traditional wealth indicators.
• Design and implement donor segmentation frameworks using appropriate statistical and machine learning techniques.
• Create propensity models that forecast giving likelihood, optimal ask amounts, retention probability, and other donor analytics metrics.
• Evaluate and enhance model performance through appropriate validation and A/B testing.
• Collaborate with business intelligence, data engineering, and professional services teams to integrate machine learning models into products and offerings.
• Stay up to date with the latest advancements in data science, machine learning, and AI.
• Document and communicate findings, methodologies, and insights to both technical and non-technical stakeholders.
• Partner closely with technology and data team members to build knowledge of existing models and fulfill requests related to our predictive analytics products and services.
• Apply modern data engineering and data science techniques to support the development of decision support products and services.
• Evaluate and analyze client outcomes data to quantify ROI and develop client success metrics
• Identify, design, and implement internal process improvements, including re-designing infrastructure for greater scalability, optimizing data delivery, and automating manual processes.
• Proactively anticipate, identify, and solve issues concerning data management to improve data quality.
• Work with stakeholders including executive, data, design, product, and services teams, assisting them with data-related technical issues.
• Support and participate in the company’s efforts to improve continuous integration, test-driven development, and production deployment frameworks.
• Work with clients (on the phone, over email, face-to-face) as needed to understand existing issues or new requirements.
• Perform quality checks from business requirements to user acceptance testing.
• Seek out innovative ways to enhance products and services offered.
• In partnership with the team, develop and maintain standardization of documents and materials.
• Other duties as assigned.

• Bachelor’s Degree in Statistics, Mathematics, Economics, Computer Science, or a related quantitative field required.
• Master’s or Ph.D in Applied Statistics, Data Science, or Data Analytics preferred.
• Minimum 5 years of experience in data science, predictive modeling, and/or marketing analytics.  Experience in the healthcare philanthropy industry preferred.
• Proven experience developing, implementing, and supporting various predictive modeling techniques in a production environment.
• Proficiency with advanced SQL, including window functions, CTEs, subqueries, complex joins, and optimization techniques for large datasets.
• Proficiency with data manipulation and analysis tools in Python such as Pandas, NumPy, Scikit-learn.
• Proficiency with statistical  analysis techniques using R, Python, SAS, SPSS, and others. .
• Deep knowledge across a variety of machine learning techniques (clustering, decision trees, neural networks, etc.) and their advantages/drawbacks.
• Deep knowledge of advanced statistical and probability theory, techniques, and concepts and the ability to apply them to business problems
• Experience working in cloud-native environments, particularly Microsoft Azure, including data warehouses, machine learning services, and data processing pipelines.

GOBEL offers compensation in the range of $130,000 – $150,000 based on experience. In addition, GOBEL offers excellent healthcare, including company-paid employee coverage for medical, dental, vision, prescription coverage, disability, and life insurance, and retirement savings plan (401k) with a company match. We also provide twenty (20) days of annual paid time off, and twelve (12) paid holidays.

(Full Time / Remote, USA)

GOBEL is an equal opportunity employer. We are committed to creating an inclusive environment and ensuring fair treatment for all employees and applicants. We do not discriminate on the basis of race, color, religion, sex (including pregnancy, sexual orientation, and gender identity), national origin, age, disability, veteran status, marital status, genetic information, physical appearance, or any other characteristic protected by applicable federal, state, or local laws.

Work samples and manager references may be requested as part of the interview process.