[Hiring] Program Manager, Data Science Core & GenAI Studio @College Board

Role Description

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.

Responsibilities

• 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.

Qualifications

• 8 or more years of experience leading complex programs or portfolios in data science, analytics, machine learning, software, research, or another technical environment.
• A record of delivering cross-functional programs from initial problem framing through implementation, adoption, and measurement.
• Familiarity with 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 creating unnecessary process.
• Sound portfolio and prioritization practices to balance strategic value, near-term commitments, team capacity, technical constraints, and risk.
• Skilled at identifying and managing cross-team dependencies, surfacing issues early, and driving resolution across teams you do not directly manage.
• Clear communication with executives, technical teams, researchers, and business partners, explaining technical tradeoffs, uncertainty, and data or model limitations in plain language.
• Familiarity with data quality, privacy, governance, security, and responsible AI considerations.
• Effective use of program management and collaboration tools, adapting agile, iterative, or stage-gated practices to the needs of the work.
• Strong judgment, curiosity, accountability, and a commitment to inclusive collaboration and continuous learning.

Requirements

• 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.
• Clear and concise communication skills, written and verbal.
• A learner's mindset and a commitment to growth.
• A drive for impact and excellence.
• A collaborative and empathetic approach.
• Ability to travel 3 to 5 times per year to New York City or Reston, Virginia.
• Authorization to work in the United States.

Benefits

• A meaningful career, a supportive team, and a comprehensive package designed to help you thrive.
• Fair and competitive compensation grounded in your qualifications, experience, impact, and the market.
• Hiring range for this role is $120–$170, adjusted by location.
• Open, transparent conversations about compensation, benefits, and work culture throughout the hiring process.