Director, Applied AI & Data Science

Vantor Services; Herndon (VA); On-site; USD 180,000 - 240,000; Full time

Job summary

Vantor Services in Herndon, VA is seeking a visionary Applied AI Science Leader to grow a high-performing team and advance geospatial AI, multimodal learning, and production ML capabilities. You will define roadmaps, translate customer and mission needs into priorities, and ensure safe, scalable AI deployments across organizations.

This role requires proven leadership, experimental rigor, and the ability to drive impact from research to production while collaborating across research, product,

Responsibilities

• Lead, grow, and develop a high-performing team of Applied AI Scientists working across geospatial AI, multimodal learning, vision-language models, LLMs, reasoning systems, predictive modeling, and production ML.
• Define and execute the Applied AI Science roadmap, translating ambiguous customer, market, and mission needs into clear technical priorities, measurable milestones, and deployable AI capabilities.
• Guide the design and productionalization of AI-driven applications that transform large-scale geospatial, imagery, structured, and contextual data into actionable insights, predictive signals, and decision intelligence.
• Champion the adoption of agentic AI as a force multiplier for the Applied AI Science organization, using AI agents to accelerate research, experimentation, evaluation, documentation, code review, and production support.
• Identify and automate high-friction scientific workflows, reducing cycle time from hypothesis to validated production capability while improving output quality and repeatability.
• Establish standards for safe, reliable, and auditable use of AI agents across model development, experimentation, evaluation, and operational workflows.
• Drive the vision for a near-zero-latency intelligence culture, where critical signals are detected, interpreted, and delivered to decision-makers with minimal delay.
• Build a proactive intelligence operating model that identifies market shifts, customer behavior changes, user behavior trends, geopolitical signals, operational risks, and emerging opportunities before they are visible through traditional reporting.
• Oversee automated insight-generation systems, self-service intelligence platforms, decision-support tools, and AI-powered workflows that empower Product, Engineering, Sales, Customer Success, Operations, and Executive Leadership.
• Establish the company-wide standard for experimentation, model evaluation, and evidence-based decision-making across AI, analytics, and product development.
• Serve as a senior authority on complex evaluation problems, including causal inference, counterfactual analysis, A/B testing, benchmark design, offline model evaluation, human-in-the-loop assessment, and launch readiness.
• Define rigorous evaluation criteria for Generative AI, agentic AI, multimodal AI, geospatial models, predictive systems, and ML-enabled product capabilities, ensuring models are reliable, robust, explainable, and fit for production.
• Oversee end-to-end AI/ML pipelines, including data ingestion, preprocessing, feature engineering, training, evaluation, inference, monitoring, and retraining.
• Partner with Engineering to move models from prototype to production with clear ownership across deployment, model serving, monitoring, incident response, lifecycle management, and continuous improvement.
• Ensure AI systems are built for scalability, latency, cost efficiency, security, reliability, reproducibil

Job description

Vantor is forging the new frontier of spatial intelligence, helping decision makers and operators navigate what's happening now and shape what's coming next. Vantor is a place for problem solvers, changemakers, and go-getters-where people are working together to help our customers see the world differently, and in doing so, be seen differently. Come be part of a mission, not just a job, where you can: Shape your own future, build the next big thing, and change the world.

To be eligible for this position, you must be a U.S. Person, defined as a U.S. citizen, permanent resident, Asylee, or Refugee.

Export Control/ITAR: Certain roles may be subject to U.S. export control laws, requiring U.S. person status as defined by 8 U.S.C. 1324b(a)(3).

Please review the job details below.

Leadership Profile

The successful candidate will be:

• Outcome-oriented. Focused on shipping AI capabilities that create measurable customer, product, and business impact.
• AI-native. Energized by using agentic AI to transform how scientific, analytical, and engineering work gets done.
• Technically credible. Able to challenge assumptions, review architectures, guide model strategy, and earn the respect of senior scientists and engineers.
• Experimentation-driven. Skilled at using evidence, causal reasoning, and rigorous evaluation to make better product and technical decisions.
• Operationally disciplined. Able to create systems, rituals, metrics, and accountability mechanisms that improve execution.
• Decisive. Willing to stop low-impact work, redirect resources, and make hard prioritization calls based on evidence.
• Customer-oriented. Able to connect technical decisions to real-world operational value and mission outcomes.
• Collaborative. Comfortable operating across research, product, engineering, infrastructure, sales, customer, and executive teams.

Responsibilities

Applied AI Science Leadership

Lead, grow, and develop a high-performing team of Applied AI Scientists working across geospatial AI, multimodal learning, vision-language models, LLMs, reasoning systems, predictive modeling, and production ML.

Define and execute the Applied AI Science roadmap, translating ambiguous customer, market, and mission needs into clear technical priorities, measurable milestones, and deployable AI capabilities.

Guide the design and productionalization of AI-driven applications that transform large-scale geospatial, imagery, structured, and contextual data into actionable insights, predictive signals, and decision intelligence.

Agentic AI Adoption & Team Acceleration

Champion the adoption of agentic AI as a force multiplier for the Applied AI Science organization, using AI agents to accelerate research, experimentation, evaluation, documentation, code review, and production support.

Identify and automate high-friction scientific workflows, reducing cycle time from hypothesis to validated production capability while improving output quality and repeatability.

Establish standards for safe, reliable, and auditable use of AI agents across model development, experimentation, evaluation, and operational workflows.

Near-Zero-Latency Intelligence & Decision Systems

Drive the vision for a near-zero-latency intelligence culture, where critical signals are detected, interpreted, and delivered to decision-makers with minimal delay.

Build a proactive intelligence operating model that identifies market shifts, customer behavior changes, user behavior trends, geopolitical signals, operational risks, and emerging opportunities before they are visible through traditional reporting.

Oversee automated insight-generation systems, self-service intelligence platforms, decision-support tools, and AI-powered workflows that empower Product, Engineering, Sales, Customer Success, Operations, and Executive Leadership.

Experimentation, Evaluation & Causal Reasoning

Establish the company-wide standard for experimentation, model evaluation, and evidence-based decision-making across AI, analytics, and product development.

Serve as a senior authority on complex evaluation problems, including causal inference, counterfactual analysis, A/B testing, benchmark design, offline model evaluation, human-in-the-loop assessment, and launch readiness.

Define rigorous evaluation criteria for Generative AI, agentic AI, multimodal AI, geospatial models, predictive systems, and ML-enabled product capabilities, ensuring models are reliable, robust, explainable, and fit for production.

Production ML, Infrastructure & Operational Excellence

Oversee end-to-end AI/ML pipelines, including data ingestion, preprocessing, feature engineering, training, evaluation, inference, monitoring, and retraining.

Partner with Engineering to move models from prototype to production with clear ownership across deployment, model serving, monitoring, incident response, lifecycle management, and continuous improvement.

Ensure AI systems are built for scalability, latency, cost efficiency, security, reliability, reproducibil