Program Manager, Data Science Core & GenAI Studio

College Board • Remote - Virginia, VA

4d • $120,000 - $170,000 • Remote

About The Position

The Program Manager, Data Science supports College Board’s Data Science Core and Gen AI Studio teams by creating the structure and operating discipline needed to deliver a growing data science portfolio. The role connects strategy to execution by managing roadmaps, priorities, dependencies, risks, decisions, and communications across workstreams. It ensures projects are ready for data science delivery, with prerequisites confirmed, dependencies explicit, and decisions made on time.

The Program Manager partners with data scientists, data and software engineers, product managers, researchers, and business leaders to turn ambiguous goals into executable plans, maintain momentum through changing conditions, and make tradeoffs visible. The role does not lead data science work, but requires enough technical fluency to understand the data science lifecycle and help specialists do their best work. Success is measured by the team’s ability to focus on high-value work, deliver predictably, manage risk early, and build trust with partners.

Responsibilities include establishing operating rhythms for planning, status, decisions, risk review, and retrospectives; coordinating cross-functional execution; building and maintaining integrated roadmaps; leading intake and prioritization; ensuring readiness criteria are met; communicating progress and technical considerations to stakeholders; and strengthening team effectiveness through capacity planning, focus, documentation, and continuous improvement. The position is fully remote, with hybrid work available to candidates near College Board offices. Travel to New York City or Reston, Virginia, is required three to five times per year.

Requirements

• At least 8 years of experience leading complex programs or portfolios in data science, analytics, machine learning, software, research, or another technical environment.
• Record of delivering cross-functional programs from initial problem framing through implementation, adoption, and measurement.
• Familiarity with, or ability to quickly develop 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.
• Ability to translate ambiguous goals into clear plans, milestones, ownership, dependencies, risks, and decisions without unnecessary process.
• Sound portfolio and prioritization practices for balancing strategic value, near-term commitments, team capacity, technical constraints, and risk.
• Ability to identify and manage cross-team dependencies, surface issues early, and drive resolution across teams not directly managed.
• Clear communication with executives, technical teams, researchers, and business partners, including explaining technical tradeoffs, uncertainty, and data or model limitations in plain language.
• Familiarity with data quality, privacy, governance, security, and responsible AI considerations, and judgment about when to involve appropriate experts.
• Effective use of program management and collaboration tools and ability to adapt agile, iterative, or stage-gated practices to the work.
• Strong judgment, curiosity, accountability, inclusive collaboration, and commitment to continuous learning.
• Passion for expanding educational and career opportunities and for mission-driven work.
• Curiosity and enthusiasm for emerging technologies, willingness to experiment with and adopt AI-driven solutions, and comfort independently learning and applying digital tools.
• Clear and concise written and verbal communication skills.
• Learner’s mindset, including welcoming diverse perspectives, giving and receiving timely respectful feedback, and improving through iterative learning and user input.
• Drive for impact and excellence, including solving complex problems, making data-informed decisions, prioritizing effectively, and continuously improving.
• Collaborative and empathetic approach, including working across differences and fostering trust.
• Authorization to work in the United States.
• Ability to travel three to five times per year to New York City or Reston, Virginia.

Responsibilities

• Establish lightweight, consistent operating rhythms for planning, status, decision-making, risk review, and retrospectives.
• Drive alignment across data science, engineering, and product stakeholders, communicating dependencies and decisions 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 and use evidence to improve plans and processes.
• Maintain program artifacts, including roadmaps, dependency maps, decision logs, risk registers, and executive-ready updates.
• 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 technical or domain experts’ judgment.
• Manage handoffs from research and prototyping through production delivery, adoption, and operational ownership.
• Translate Data Science strategy and business priorities into programs, workstreams, milestones, and measurable outcomes.
• Own integrated roadmaps and planning processes, sequencing research priorities and tracking progress from experimentation through deployment.
• Lead intake and prioritization with Data Science leaders, balancing impact, effort, capacity, risk, and sequencing.
• Establish readiness criteria, including confirmed 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 ownership, decision rights, and escalation paths.
• Build shared understanding with technical and non-technical leaders about objectives, constraints, tradeoffs, and progress.
• Communicate data science work clearly, including uncertainty, model limitations, and responsible-use considerations.
• Create stakeholder and user feedback loops and plan for solution adoption from the beginning.
• Prepare concise recommendations and decision materials for leaders.
• Partner with Data Science leadership on capacity planning, team growth, role clarity, and sustainable delivery practices.
• Protect team 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.
• Travel three to five times per year to New York City or Reston, Virginia.

Benefits

• Comprehensive package designed to support employees
• Fair and competitive compensation based on qualifications, experience, impact, and market data
• Location-adjusted salary

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed