Vice President, Data Science

As Vice President of Data Science, you will lead and grow our in-house data science team. This team is responsible for research, experimentation, data collection and curation, and data analysis that contributes to the performance of Five9’s AI products. Tasks include evaluation of and selection of AI agent architectural frameworks, evaluation and comparison of LLM models across commercial and open source choices, model fine-tuning for dedicated tasks, prompt engineering, prompt structure and design, and composite model definitions and evaluations. The scale of Five9 provides a wealth of data that data science team has access to. The data science team is very much applied – their work directly makes its way into real products providing direct customer benefit. 

As lead of this team, you will take complete ownership of the technical and operational direction of the organization, including growing to team to meet increased demand for its capabilities. 

Key Responsibilities:

• Technical Direction Setting: As an expert in the leading edge of AI and data science, you will direct the team on the methodologies, practices, algorithms, experiments and processes they perform.
• Hands On: You are expected to also be hands on, not just a manger, and be directly responsible for some amount of the technical work in addition to directing the team.
• Organizational Growth: You will be tasked with  growing the team, and ensuring we have the right talent to accomplish our goals.
• Collaborator and Spokesperson: You will act as an internal and external spokesperson for data science, and collaborate with stakeholders across the company. Internally, you will be expected to meet with product managers, executives and estaff, and be able to converse effectively with them. You will also occasionally meet with customers to understand how Five9 products, and the data science behind them, impacts the customers. You are expected to participate in industry activities, including publication of blog posts and papers, along with participation in AI conferences. 

Technical Expertise:

• 12+ years experience in data science or AI applied research, ideally at a best-in-class applied research organization.

• Deep Expertise in Modern AI/ML
• Extensive hands-on experience with LLMs, agentic architectures, retrieval-augmented systems, transformers, and composite model pipelines.
• Strong understanding of commercial and open-source model ecosystems (e.g., OpenAI, Anthropic, Google, Meta, Mistral), including evaluation, benchmarking, and tradeoff analysis.
• Model Development & Optimization
• Proven ability to perform fine-tuning, supervised/unsupervised training, prompt engineering, prompt optimization, and model orchestration for real-world use cases.
• Experience designing evaluation frameworks, experiment methodologies, and robust model comparison workflows.
• Data Engineering & Curation
• Expertise in large-scale data collection, labeling, cleaning, and curation pipelines, preferably with conversational or unstructured text data.
• Familiarity with tools and techniques for data quality assessment, dataset versioning, and data governance.
• Applied Data Science & Analytics
• Strong proficiency in statistical analysis, A/B experimentation, causal inference, and performance measurement.
• Demonstrated success turning data insights into product improvements that drive measurable business outcomes.
• Software Development & Systems Thinking
• Ability to work with engineering teams using modern software practices (Python, data platforms, cloud-native environments, APIs, ML Ops tooling).
• Understanding of production ML systems, deployment patterns, monitoring, and safety/guardrail design.

People & Collaboration Skills:

• Cross-Functional Partnering
• Ability to collaborate effectively with product managers, engineering leaders, UX, and GTM teams to translate business needs into data science strategies.
• Adept at explaining complex technical concepts to executives, customers, and non-technical stakeholders.
• Communication & Storytelling
• Exceptional written and verbal communication skills, including ability to publish thought leadership (papers, blog posts) and present at conferences.
• Team Development & Mentorship
• Passion for mentoring senior and junior data scientists, fostering technical excellence, and building a culture of experimentation and rigorous thinking.
• Customer Empathy
• Experience engaging directly with customers to understand their needs, gather feedback, and translate insights into product or model improvements.

Leadership & Strategic Skills:

• Vision Setting & Direction
• Ability to define the data science strategy for AI Agents and customer experience products, aligning with corporate priorities and market opportunities.
• Hands-On Leadership
• Comfortable being an active contributor—writing code, running experiments, reviewing research—while simultaneously guiding the team’s overall direction.
• Organizational Scaling
• Experience hiring, scaling, and structuring high-performing data science teams across multiple geographies.
• Operational Excellence
• Ability to build processes for experimentation, model evaluation, data quality management, and continuous delivery of data science innovation into product.
• Executive Presence & Influence
• Skilled at influencing E-staff and senior leadership, defending technical decisions, shaping product strategy, and representing data science internally and externally.
• Ethics, Safety & Risk Awareness
• Deep understanding of responsible AI principles, privacy considerations, and model safety, including evaluating risks when operating at enterprise scale.

Educational Requirements:

Advanced degree in a quantitative or technical field, such as:

• Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Applied Mathematics, Electrical Engineering, Computational Linguistics, or a related field.
• Master’s degree in one of the above fields with significant applied industry experience in AI/ML leadership roles.