Data Science Senior Analyst, AI Engineer

The Ohio State University•Columbus, OH

About The Position

The Ohio State University’s Office of Advancement seeks a curious, creative, and collaborative Data Science Senior Analyst to use data science and emerging AI capabilities to help staff better understand alumni, donors, and other constituents, make informed decisions, and improve their work. The role applies customer analytics, predictive modeling, machine learning, generative AI, statistical methods, and other appropriate techniques to support fundraising, engagement, marketing, operational improvement, and strategic decision-making. Work may include identifying and prioritizing audiences, predicting behavior, analyzing complex data, and creating AI-enabled tools that make information and expertise more accessible to staff. The Data Science Senior Analyst coordinates and executes assigned solutions from discovery and prototyping through deployment, adoption, monitoring, and improvement. The role works closely with intended users and technical partners to create solutions that are useful, understandable, maintainable, and responsibly designed. It independently performs complex analytical and technical work, recommends appropriate approaches, and receives general guidance on new projects or unfamiliar assignments. It also serves as a consultative resource on the capabilities and limitations of AI, data science, and analytics, communicating complex concepts to technical and nontechnical audiences. The position reports to the Associate Director, Data Science and collaborates with colleagues across Advancement, the university, and the Wexner Medical Center. The employee must reside in Ohio. A flexible work arrangement, including remote or hybrid work, may be available subject to university and unit policies and manager approval. The position is a regular, 40-hour-per-week, first-shift role and is designated as a standby position. The budget is up to $113,449.96; an offer depends on internal equity and the candidate’s qualifications.

Requirements

Bachelor’s degree in a related field, or an equivalent combination of education and experience.
Four years of relevant professional experience in data science, applied artificial intelligence, machine learning, business intelligence, reporting and analytics, customer or marketing analytics, statistical analysis, predictive modeling, data engineering, software or application development, or related quantitative and technical work.
Must reside in Ohio.
Ability to independently perform complex analytical and technical work and recommend appropriate approaches.
Position is designated as standby.
Final candidates must successfully complete a background check; a drug screen or physical may be required after an offer.

Nice To Haves

Advanced degree in data science, data analytics, statistics, applied mathematics, computer science, information systems, engineering, economics, business, marketing, human-computer interaction, or a related field, or additional relevant education and experience.
Six years of relevant experience.
Experience using programming or query languages to analyze data and develop data science, machine-learning, AI, or analytical solutions.
Experience creating predictive models or applying statistical, machine-learning, generative AI, natural language processing, or comparable methods to practical problems.
Experience working with business partners or intended users to understand needs, translate business problems into analytical or technical work, and communicate findings or solutions to technical and nontechnical audiences.
Experience using Python, R, SQL, or comparable languages for data analysis, predictive modeling, machine learning, AI, or application development; Python and SQL proficiency preferred.
Experience with cloud data and AI platforms, preferably Azure Databricks or Microsoft Azure; Apache Spark or PySpark experience is desirable.
Experience developing applied AI solutions such as agents, conversational experiences, intelligent search, retrieval-augmented generation, semantic search, tool integrations, structured outputs, or multi-step workflows.
Experience using AI-assisted or agentic development tools such as Claude Code, GitHub Copilot, Databricks Genie Code, or comparable technologies.
Experience implementing or supporting analytical, machine-learning, AI, or software solutions for business users, including application development, integration, evaluation, deployment, monitoring, documentation, training, or adoption.
Experience with development and solution-lifecycle practices such as Git or GitLab, MLflow, APIs, versioning, code review, repeatable testing, usability and accessibility practices, experimental design, or responsible AI.
Experience in higher education, Advancement, fundraising, alumni engagement, nonprofit development, marketing, customer or audience analytics, consulting, product development, or another complex, relationship-centered environment.

Responsibilities

Coordinate and execute discovery, technical design, development, testing, deployment, and ongoing improvement of assigned applied AI, machine-learning, and advanced analytics solutions.
Develop predictive models, AI solutions, and analytical approaches to understand constituents, identify and prioritize audiences, recognize opportunities, and support engagement and fundraising decisions.
Design and build AI-enabled tools, including agents, intelligent search, decision-support tools, user-facing applications, workflow automations, conversational experiences, and reusable analytical services.
Recommend methods based on documented business needs, available data, feasibility, risk, and intended outcomes; create prototypes and proofs of concept to assess feasibility and gather user feedback.
Apply appropriate statistical, machine-learning, and analytical methods, including methods such as regression, classification, clustering, recommendation, forecasting, natural language processing, information retrieval, graph analysis, and experimentation.
Develop user-facing applications, visualizations, documentation, and training materials in accordance with accessibility, usability, and audience needs.
Identify and evaluate internal and external data sources; coordinate with appropriate teams when acquisition or broader integration is needed.
Design, build, test, document, and maintain data workflows, feature pipelines, evaluation datasets, and reusable data components; assess data quality, implement validation checks, document limitations, and partner on remediation.
Develop, deploy, and maintain solutions using approved platforms and technologies, including Azure Databricks and Microsoft Azure, following organizational standards.
Develop evaluation plans and acceptance criteria; evaluate accuracy, relevance, reliability, robustness, fairness, usability, cost, latency, and business value; recommend appropriate solutions based on results.
Monitor deployed models and AI solutions for performance, quality, drift, operational health, unintended outcomes, usage, cost, and continued business value.
Document intended uses, limitations, assumptions, data dependencies, risks, evaluation results, and required human oversight; use approved AI-assisted and agentic development approaches when appropriate and validate resulting work.
Design solutions for reuse, scalability, performance, cost, and maintainability; develop reusable components and recommend practice improvements.
Build collaborative relationships with business partners and engage them throughout solution lifecycles; gather information about business processes, user needs, pain points, decisions, and desired outcomes.
Translate complex or ambiguous problems into proposed requirements, success measures, and project scopes; assess opportunities based on value, feasibility, risk, data readiness, cost, effort, dependencies, and priorities.
Provide analysis and recommendations to support stakeholder and leadership decisions about solution approaches and portfolio prioritization.
Involve intended users in design and development, gather feedback, conduct user acceptance and usability testing, and iteratively improve solutions.
Coordinate implementation, deployment planning, rollout, early-life support, workflow design, and adoption planning with business and technology partners.
Create or coordinate communications, demonstrations, methodology summaries, user guidance, training resources, and implementation documentation for understanding and adoption.
Recommend and monitor measures of adoption, usability, efficiency, and business impact; use findings and feedback to recommend improvements and inform continued use and investment.
Coordinate ongoing maintenance, monitoring, support, governance, adoption, improvement, transition, replacement, and retirement activities for assigned solutions; manage competing assignments and communicate tradeoffs, dependencies, risks, and resource needs.
Apply university policies, approved technology standards, data governance requirements, and responsible AI practices; identify privacy, security, fairness, accessibility, transparency, reliability, and appropriate-use considerations and engage specialists when needed.
Follow review and escalation processes and applicable intellectual property, licensing, data-use, and AI-generated code and content requirements.
Maintain traceability, versioning, lineage, reproducibility, metadata, definitions, quality information, and usage guidance using approved platform capabilities and documentation practices.
Use evidence, experimentation, prototypes, and clear communication to explain AI and data science capabilities, limitations, risks, and applications.
Independently complete complex work within established objectives and standards; recommend technical approaches, resolve routine project issues, and communicate risks and dependencies.
Participate in peer review and provide constructive feedback on code, pipelines, models, AI tools, evaluation methods, and documentation; share knowledge and guide less-experienced colleagues.
Apply team practices for version control, code review, testing, reproducibility, deployment, monitoring, documentation, and maintainability within GitLab, Azure Databricks, and other approved environments.
Provide technical input to data science and AI strategy, team standards, reusable patterns, responsible AI processes, and the technology environment; maintain knowledge of evolving practices and share relevant learning.
Perform other duties as assigned.