Director, Data Scientist (Predictive & Agentic AI)

SymphonyAI•New York, NY

6d•Remote

About The Position

SymphonyAI is seeking a Director of Data Science – Predictive & Agentic AI to lead the design, development, and deployment of advanced AI capabilities across our enterprise SaaS platforms. This role sits at the intersection of predictive analytics, agentic AI systems, and real-world enterprise workflows, with a strong focus on delivering measurable business outcomes for customers. This role requires both deep technical expertise and the ability to translate AI innovation into customer and business value.

Requirements

Proven experience leading enterprise-scale data science organizations delivering real-world AI products.
Deep expertise in predictive modeling, machine learning, and statistical methods, combined with hands-on experience in LLMs and agentic AI frameworks.
Strong intuition for trust, explainability, and usability in AI systems used by expert, high-stakes decision makers.
Track record of translating complex AI capabilities into measurable business impact.
Excellent communication skills, with the ability to influence both technical and non-technical stakeholders.
Passion for building inclusive teams, fostering experimentation, and driving continuous learning.

Responsibilities

Lead and mentor a high-performing, global team of data scientists, ML engineers, and applied researchers.
Define and execute the AI vision and roadmap across Predictive AI, Decision Intelligence, Agentic AI, and Generative AI.
Act as a thought leader for AI strategy, influencing executive stakeholders and product direction.
Own the development of predictive, prescriptive, and causal models, including forecasting, optimization, propensity modeling, anomaly detection, and decision scoring.
Ensure predictive models serve as the statistical foundation for agentic and generative AI workflows.
Translate probabilistic outputs into clear, actionable recommendations for enterprise users.
Drive the design and deployment of LLM-powered agents and multi-agent systems that reason, plan, and execute actions grounded in trusted data and models.
Oversee model development using LLMs, transformers, diffusion models, and hybrid architectures that combine deterministic and generative approaches.
Ensure generative AI augments human decision-making rather than replacing core analytical rigor.
Establish a robust experimentation and measurement framework, including offline validation, online A/B testing, causal inference, and counterfactual analysis.
Own model consistency, stability, and reproducibility, ensuring reliable outputs under equivalent conditions and clear signaling when uncertainty or variability is expected.
Define success metrics tied directly to business outcomes and user adoption.
Build AI systems that are explainable, transparent, and auditable, with clear decision rationale, feature attribution, and confidence scoring.
Design human-in-the-loop workflows, enabling expert users to review, override, and refine AI recommendations.
Ensure AI outputs align with the intuitive expectations of experienced domain professionals.
Partner closely with engineering, product management, and customer success to deliver scalable AI solutions in SaaS environments.
Establish best practices for MLOps, model monitoring, governance, and responsible AI, ensuring compliance, safety, and operational excellence.
Stay ahead of emerging trends in predictive modeling, agentic systems, foundation models, and AI orchestration.