Program Manager, Data Science Core & GenAI Studio

College Board - Technology - Data Science Core & GenAI Studio

Location: This is a fully remote role. Candidates who live near CB offices have the option of being fully remote or hybrid (Tuesday and Wednesday in office).

Type: This is a full-time position

About the Team

The Data Science team develops analytical approaches, statistical models, and machine learning capabilities that help College Board make better decisions and deliver stronger outcomes for students and educators. Our growing team partners across research, product, engineering, business intelligence, and operations to turn complex questions and data into useful, responsible, and scalable solutions. This role supports both Data Science Core and Gen AI Studio teams.

The work spans exploratory analysis, experimentation, model development, production delivery, and ongoing monitoring. Accuracy, transparency, privacy, and responsible use of data are essential, particularly in high-stakes education contexts. What makes this team distinctive is its combination of rigorous data science, practical problem-solving, and sustained cross-functional collaboration.

About the Opportunity

As Program Manager, Data Science, you will create the structure and operating discipline that enables a growing data science portfolio to deliver meaningful results. You will connect strategy to execution by managing roadmaps, priorities, dependencies, risks, decision points, and communication across multiple workstreams. A core part of this work is making sure projects are ready before Data Science delivery begins: prerequisites confirmed, dependencies explicit, and decisions made on time.

You will partner closely with data scientists, data and software engineers, product managers, researchers, and business leaders. You will help teams translate ambiguous goals into executable plans, maintain momentum through changing conditions, and make tradeoffs visible. You are not expected to serve as the lead data scientist; you will bring enough technical fluency to understand the data science lifecycle, ask sharp questions, and help specialists do their best work.

Success in this role will be measured by the team's ability to focus on the highest-value work, deliver reliable outcomes predictably, manage risk early, and build trust with partners.

In this role you will

Manage Delivery Risk and Program Operations (30%)

• •Establish lightweight, consistent operating rhythms for planning, status, decision-making, risk review, and retrospectives.
• •Drive alignment across data science, engineering, and product stakeholders, ensuring dependencies and decisions are communicated clearly and on time.
• •Identify delivery, data, model, compliance, and resourcing risks early; drive mitigation and escalate when timely decisions are needed.
• •Track program health and outcomes using meaningful measures rather than activity alone, and use evidence to improve plans and processes.
• •Maintain clear program artifacts, including roadmaps, dependency maps, decision logs, risk registers, and executive-ready updates.

Orchestrate Cross Functional Execution (25%)

• •Coordinate work across data science, research, product, engineering, analytics, privacy, legal, security, and business teams.
• •Build executable plans from ambiguous goals and align contributors on scope, responsibilities, milestones, dependencies, and acceptance criteria.
• •Facilitate decisions and resolve blockers without replacing the judgment of technical or domain experts.
• •Manage handoffs from research and prototyping through production delivery, adoption, and operational ownership.

Drive Program Strategy and Portfolio Planning (20%)

• •Translate Data Science strategy and business priorities into a clear portfolio of programs, workstreams, milestones, and measurable outcomes.
• •Own integrated roadmaps and planning processes, translating research priorities into sequenced milestones and tracking progress from experimentation through deployment.
• •Lead intake and prioritization in partnership with Data Science leaders, balancing impact, effort, capacity, risk, and sequencing.
• •Establish readiness criteria so projects have confirmed prerequisites, including data access, legal and privacy review, a named business owner, and agreed success measures, before Data Science delivery begins.
• •Define program success measures and establish clear ownership, decision rights, and escalation paths.

Build Stakeholder Alignment and Adoption (15%)

• •Serve as a trusted partner to technical and non-technical leaders, creating shared understanding of objectives, constraints, tradeoffs, and progress.
• •Communicate complex data science work in clear language appropriate to the audience, including uncertainty, model limitations, and responsible-use considerations.
• •Create feedback loops with users and stakeholders so that solutions address real needs and adoption is planned from the beginning.
• •Prepare concise recommendations and decision materials that help leaders act with the right context.

Strengthen Team Effectiveness and Scale (10%)

• •Partner with Data Science leadership on capacity planning, team growth, role clarity, and sustainable delivery practices.
• •Protect focus by reducing avoidable interruptions, clarifying priorities, and improving how work enters and moves through the team.
• •Promote documentation, reuse, knowledge sharing, and continuous improvement across programs.
• •Support an inclusive, accountable environment in which people can raise risks, challenge assumptions, and learn from outcomes.

About you

• •You have 8 or more years of experience leading complex programs or portfolios in data science, analytics, machine learning, software, research, or another technical environment.
• •You have a record of delivering cross-functional programs from initial problem framing through implementation, adoption, and measurement.
• •You are familiar with, or can quickly build working knowledge of the data science lifecycle, including data readiness, experimentation, model development and validation, deployment, monitoring, and iteration; hands-on model development expertise is not required.
• •You can translate ambiguous goals into clear plans, milestones, ownership, dependencies, risks, and decisions without creating unnecessary process.
• •You use sound portfolio and prioritization practices to balance strategic value, near-term commitments, team capacity, technical constraints, and risk, including in settings where impact is measured by mission and stakeholder outcomes rather than revenue.
• •You are skilled at identifying and managing cross-team dependencies, surfacing issues early, and driving resolution across teams you do not directly manage.
• •You communicate clearly with executives, technical teams, researchers, and business partners and can explain technical tradeoffs, uncertainty, and data or model limitations in plain language.
• •You are familiar with data quality, privacy, governance, security, and responsible AI considerations and know when to bring the appropriate experts into a decision.
• •You use program management and collaboration tools effectively and can adapt agile, iterative, or stage-gated practices to the needs of the work.
• •You demonstrate strong judgment, curiosity, accountability, and a commitment to inclusive collaboration and continuous learning.

All roles at College Board require:

• •A passion for expanding educational and career opportunities and mission-driven work
• •Curiosity and enthusiasm for emerging technologies, with a willingness to experiment with and adopt new AI-driven solutions and comfort with learning and applying new digital tools independently and proactively.
• •Clear and concise communication skills, written and verbal
• •A learner's mindset and a commitment to growth: welcoming diverse perspectives, giving and receiving timely, respectful feedback, and continuously improving through iterative learning and user input.
• •A drive for impact