Senior Data Scientist - Applied AI

Blackbaud

1d•Remote

About The Position

Senior Data Scientist – Applied AI Blackbaud is seeking a high-performing Senior Data Scientist to champion and accelerate AI innovation in product development. The senior data scientist will collaborate closely with the data science team, engineering teams, and product managers to design and evaluate next generation AI solutions that deliver measurable impact to customers while meeting high standards for trust, transparency, and responsibility. This role will solve complex problems, applying the right approach to each problem – ranging from machine learning to Retrieval-Augmented-Generation (RAG), selective fine-tuning, or task-specific small language models (SLMs), based on impact, risk, cost, and operational context.

Requirements

5+ years of experience in data science or applied machine learning, with experience in large-language based systems (Microsoft and Databricks preferred).
Strong grounding in statistics and ML foundations.
Experience applying multiple modeling approaches to production problems.
Ability to explain tradeoffs between accuracy, explainability, cost, risk.
Clear communicator with technical and non-technical partners.
Experience with data analysis in Python, R or similar language.
Proven experience influencing business decisions and driving business value.
Ability to work independently and take initiative with little oversight.
Demonstrated commitment to responsible AI.
Strong bias to action demonstrated by proactively moving work forward, making timely decisions with incomplete information.
Ability to deliver work that meets all minimum standards of quality, security, and operability.

Responsibilities

Frame business and customer problems into responsible data science and ML solutions.
Design, train, and evaluate models across a range of techniques, selecting the appropriate fit (rules, classical ML, RAG, fine-tuning, SLMs) based on problem fit.
Design and curate high-quality datasets for modeling and evaluation.
Define success metrics and evaluation approaches, including human-in-the-loop where appropriate.
Clearly articulate tradeoffs between approaches (accuracy, explainability, cost, latency, risk).
Perform error analysis to understand model behavior, bias, and failure modes.
Partner with product management, AI platform engineering, engineering, and governance teams to operationalize solutions responsibly.
Contribute to model documentation, transparency, artifacts, and ongoing risk management.
Improve AI processes through iterative testing, monitoring, measurement, and automation.
Present key findings and insights clearly to technical and non-technical audiences at various levels of the organization.
Mentor other team members in data science methodologies and participate in cross-team trainings.
Stay abreast of the latest advancements in data and AI technologies, suggesting new capabilities to incorporate into the AI platform.
Actively contribute to thought leadership through research, articles, presentations, and collaboration with Blackbaud Institute partners.