As the
AVP, Data Science, you provide hands-on technical leadership for advanced analytics and AI across the organization.; This is a senior technical leadership role focused on setting technical direction, building production-grade solutions, and elevating how data science is practiced at scale across the organization.
You will lead the design, development, and productionization of large-scale, enterprise-grade data science and AI solutions, while owning company-wide data science and AI engineering standards. A core mandate of the role is to modernize the software development lifecycle (SDLC) through AI-native development practices, including the adoption of AI-assisted coding tools (e.g., OpenAI Codex, Anthropic Claude, and similar LLM-based programming agents) to materially improve productivity, quality, and delivery speed. You will also lead the Data Science Community of Practice, shaping technical direction, mentoring senior practitioners, and establishing clear quality bars across teams.
What you will do:- Enterprise Technical Leadership: Serve as the senior technical authority and expert for data science and applied AI. Lead hands-on architecture, modeling, and implementation of complex, production-grade ML and AI solutions.
- Production-Grade Delivery: Lead and own the design and deliver scalable, reliable, and maintainable data science solutions that operate in real-world production environments-not prototypes or experiments.
- AI-Assisted Development & SDLC Ownership: Own and modernize the data science SDLC end-to-end. Establish AI-native development workflows using tools such as Codex, Claude, and similar agents to improve developer throughput, code quality, testing, and documentation.
- Standards & Best Practices: Define, enforce, and evolve company-wide data science and AI engineering standards, including coding standards, model development practices, MLOps patterns, and peer review expectations.
- Community of Practice Leadership: Build and lead the Data Science Community of Practice across the organization. Set technical direction, facilitate knowledge sharing, mentor senior data scientists, and establish consistent quality bars for methods, code, and outputs.
- Consistency, Reuse & Efficiency: Drive consistency and efficiency across teams through reusable libraries, reference architectures, shared tooling, and governance-lite standards that enable speed without bureaucracy.
- Cross-Functional Partnership: Partner closely with Data Engineering, platform teams, and business leaders to ensure data science solutions are production-ready, scalable, and aligned with business priorities. Influence roadmaps and technical decisions without relying on formal reporting lines.
- Lead, coach, mentor, and develop your team members to build capability, drive performance, and support their ongoing growth and career progression.
What you will bring:- 15+ years of experience in data science, machine learning, or applied AI, with substantial experience building and productionizing large-scale solutions in complex environments.
- Deep hands-on expertise in modern data science and ML development, including strong software engineering practices (e.g., Python, version control, testing, CI/CD, MLOps).
- Demonstrated expertise using AI-assisted programming tools (e.g., Codex, Claude, GPT-based coding agents) to improve developer productivity and engineering quality.
- Proven ability to lead technically across multiple teams, influencing standards, architecture, and delivery without being a line manager.
- Strong communication skills with the ability to translate complex technical concepts for senior technical and non-technical stakeholders.
- Preferred experience standing up or leading a Data Science Center of Excellence or Community of Practice.
- Familiarity with cloud-based ML platforms and modern data ecosystems.
- Track record of driving measurable improvements in delivery speed, quality, or reuse through tooling, standards, or SDLC modernization.
- Advanced degree in a quantitative or technical field (Master's or PhD) is a plus.
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We may use artificial intelligence tools as part of our recruitment process to assist in the initial screening of resumes. All hiring decisions, including candidate evaluation, selection, and disposition, are made by human recruiters.
