VP, Product Management

Uniphore•Palo Alto, CA

3d•$286,400 - $358,000•Onsite

About The Position

The Vice President of Data Products is responsible for overseeing the vision, strategy, and execution of the company’s data platforms. Our data platforms play multiple roles, including: As internal capabilities, they power our GenAI stack (access, transform, and supply for GenAI model training data), our CDP product and our applications. As customer-facing products, they provide Zero-ETL access to Enterprise data and a rich suite of capabilities to connect, clean, orchestrate and operationalize data. Our Zero Data AI Cloud, positioned around the concept of being able to leverage Enterprise data more quickly and flexibly than any other approach, is anchored on this suite of data platforms that this role will inherit and expand. This is the GM of the Data Product business and ensures the development and delivery of innovative data products and solutions that align with business objectives, enhance decision-making and create competitive advantages. The VP leads cross-functional teams to design, build, and maintain scalable data products, deliver on revenue goals, and while fostering a data-driven culture.

Requirements

Bachelor’s degree in Computer Science, Data Science, AI, or a related field; Master’s degree in Databases or Machine learning technology and PHD would be ideal or MBA preferred.
10+ years of experience in data product management, AI, or analytics, with at least 5 years in a senior leadership role.
Proven track record of delivering successful data products at scale which includes AI-driven data product development and readiness initiatives.
Strong understanding of data platforms, analytics tools, and machine learning technologies, including: Database architectures, including query systems, query optimization, query languages and abstractions.
Data storage including table formats such as Iceberg and Delta Lake, and cloud storage technologies including huyperscalar products like S3.
Machine Learning including basic ML algorithms, ML development lifecycle, use cases of different ML techniques in the enterprise.
Understanding of ML tools and how they are deployed in Enterprises, such as Spark, Python, Jupyter Notebook, and Gen AI tools such as PyTorch, LangChain, HuggingFace, etc.
Deep understanding of Enterprise Data Management, including Technologies like Databases, MDM, ETL/Data Orchestration/Data Annotation, Data Observability, etc.
Logical/Physical schemas, views, replication techniques, etc.
Architectures such as EDWs, Data Marts, Data Lakes / Lakehouses, ML playgrounds, etc.
Governance concepts such as data security, data sovereignty, data privacy, data governance frameworks, etc.
Expertise in product management frameworks and agile development methodologies.
Excellent communication and stakeholder management skills.
Ability to balance strategic vision with operational execution.
Strong leadership and people management capabilities.
Innovative mindset with a customer-centric approach.
Ability to manage competing priorities in a fast-paced environment.
Strategic thinker with strong problem-solving and decision-making capabilities.
Excellent communication and stakeholder management skills.
Ability to balance strategic vision with operational execution.

Nice To Haves

Direct experience with Data Warehousing design/deployment/management in an enterprise environment a plus.
Deep understanding of Large Language Models (LLMs) and Generative AI architectures, including transformer-based models, embeddings, prompt/context engineering, model selection, fine-tuning, RAG, and the tradeoffs between proprietary and open-source models.
Expertise in emerging agentic AI architectures, including AI agents, tool/function calling, orchestration, memory, multi-agent systems, workflow automation, and human-in-the-loop patterns, with the ability to translate these capabilities into scalable enterprise products.
Deep understanding of evaluating and operationalizing AI/LLM systems in production, including model and application evaluation, quality measurement, latency, cost/performance optimization, observability, model drift, feedback loops, and continuous improvement.
Strong understanding of enterprise AI safety, security, and responsible AI practices, including AI governance, model risk, prompt injection, data leakage, bias, privacy, guardrails, access controls, explainability, and human oversight.

Responsibilities

Define and execute the strategic roadmap for data platforms, aligning with the company’s business goals, financial outcomes, and customer needs.
Develop and execute a data readiness strategy to support internal activities aligned with the Engineering and AI organization's architecture approaches and support Customer discovery and insights from their AI goals.
Collaborate with executive leadership to identify and prioritize data innovation, AI use cases, and monetization that require high-quality, accessible, and reliable data products.
Build and communicate a vision for how data products can deliver business value and drive growth.
Ensure that all data platforms provide robust capabilities, assets are for cleaning, annotated, structured, and optimized for AI and machine learning purposes.
Drive the adoption of modern data management practices, including data labeling, feature engineering, and real-time data streaming for AI within Uniphore and supporting sales during customer interactions.
Partner with data scientists and engineers to define requirements and participate with Engineering and Architecture teams in designing scalable AI pipelines and MLOps workflows.
Lead the high-level design and collaborate with AI Engineering and Data Engineering Leadership, define development, and deployment of scalable requirements, user-centric data products and platforms.
Prioritize product features and enhancements based on customer feedback, market trends, and business needs for monetization Ensure the use of best practices in data science, analytics, and engineering in product design.
Oversee the creation and deployment of data products, including training datasets, feature stores, and synthetic data generation tools, that enable AI innovation.
GTM experience working with Sales, performing competitor analysis, pricing, experience working with Product Marketing and partner teams on expanding product growth and delivering on revenue targets.
Ability to define a product roadmap, prioritize feature, functionality, and capabilities that are needed by customers, based on the competitive landscape, analyst reports, etc.
Build, mentor, and lead a multidisciplinary team of product managers, data engineers, and AI specialists.
Foster collaboration across engineering, design, sales, marketing, and other departments.
Promote a culture of continuous learning, experimentation, and data-driven decision-making.
Create a collaborative culture that bridges data engineering, product development, and AI research teams.
Develop skill-building programs to keep the team up-to-date with the latest AI and data trends.
Partner with engineering teams to ensure seamless integration of data products – data lakes, warehouse, AI-specific data pipelines existing systems.
Stay informed on emerging technologies and trends in big data, AI/ML, and analytics.
Champion with the products of AI-ready technologies such as automated data labeling tools, data versioning systems, and explainable AI frameworks.
Evaluate and incorporate emerging technologies to enhance data readiness for AI, such as synthetic data platforms or edge AI solutions.
Ensure data products take into practices that adhere to global regulations (e.g., GDPR, CCPA) and ethical AI standards.
Products enforce governance policies for data quality, lineage, and versioning critical for AI integrity.
Collaborate with legal and compliance teams to manage risks associated with sensitive or biased data during the product development and sales process.