Program Manager, Data Science Core & GenAI Studio
New
C
College BoardEducation technology
Source API remote eligibility restrictions: United StatesFull-TimeManager
Salary120,000 - 170,000 USD per year
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Job Details
- Experience
- 8 or more years of experience leading complex programs or portfolios in data science, analytics, machine learning, software, research, or another technical environment.
- Required Skills
- AgileMachine LearningRisk Management
Requirements
- Have 8 or more years of experience leading complex programs or portfolios in data science, analytics, machine learning, software, research, or another technical environment.
- Demonstrate cross-functional program delivery from initial problem framing through implementation, adoption, and measurement.
- Have or be able to quickly build working knowledge of the data science lifecycle, including data readiness, experimentation, model development and validation, deployment, monitoring, and iteration.
- Translate ambiguous goals into plans with milestones, ownership, dependencies, risks, and decisions.
- Apply portfolio and prioritization practices that balance strategic value, commitments, capacity, technical constraints, and risk.
- Identify and manage cross-team dependencies, surface issues early, and drive resolution across teams.
- Communicate technical tradeoffs, uncertainty, and data or model limitations clearly to executives, technical teams, researchers, and business partners.
- Be familiar with data quality, privacy, governance, security, and responsible AI considerations.
- Use program management and collaboration tools effectively and adapt agile, iterative, or stage-gated practices to the work.
- Travel 3 to 5 times per year to New York City or Reston, Virginia.
Responsibilities
- Establish operating rhythms for planning, status, decision-making, risk reviews, and retrospectives.
- Align data science, engineering, product, and other stakeholders on dependencies, decisions, scope, responsibilities, milestones, and acceptance criteria.
- Identify delivery, data, model, compliance, and resourcing risks; drive mitigation and escalate when decisions are needed.
- Maintain roadmaps, dependency maps, decision logs, risk registers, and executive-ready updates.
- Translate strategy and business priorities into programs, workstreams, milestones, and measurable outcomes.
- Lead intake and prioritization, balancing impact, effort, capacity, risk, and sequencing.
- Establish project readiness criteria, including data access, legal and privacy review, a named business owner, and agreed success measures.
- Manage handoffs from research and prototyping through production delivery, adoption, and operational ownership.
- Communicate technical tradeoffs, uncertainty, model limitations, and responsible-use considerations to stakeholders.
- Partner with Data Science leadership on capacity planning, team effectiveness, and continuous improvement.
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