Customer Intelligence Engineer

📋 Description

• • As a Customer Intelligence Engineer at Seeq Corporation, you will play a pivotal role in empowering the Customer Success organization with actionable insights derived from customer data. This is a unique opportunity to operate at the dynamic intersection of product usage analytics, customer health modeling, workflow automation, and applied artificial intelligence, contributing directly to Seeq's strategic customer engagement and retention efforts.
• • Your primary mission will be to transform raw customer data—encompassing usage patterns, engagement metrics, and contract signals—into a stream of actionable intelligence. This intelligence will be delivered precisely to the right individuals at the opportune moments, enabling proactive and strategic decision-making across the Customer Success team.
• • This role is designed for early-career professionals with high potential, making it an ideal launchpad for engineering graduates or former process engineers looking to pivot into the exciting fields of analytics and software development. You will be instrumental in building scalable intelligence workflows that allow Seeq’s Customer Success organization to act earlier, smarter, and more strategically.
• • A core responsibility will be the design and deployment of account-level Slack summaries. These summaries will provide Customer Success Managers (CSMs) and leadership with concise, relevant updates on customer status, engagement, and potential areas of concern or opportunity, fostering better communication and faster response times.
• • You will be tasked with translating complex product usage data and contract information into clear, operational signals. This involves identifying key metrics and patterns that indicate customer health, adoption levels, and potential churn risks or expansion opportunities.
• • Collaboration with the Analytics Engineering team will be crucial as you build or extend data pipelines. This partnership ensures the integrity, scalability, and efficiency of the data infrastructure that underpins your intelligence workflows.
• • Developing Python-based automation will be a significant part of your role, enabling the operationalization of insights at scale. This means creating robust scripts and applications that can process data, generate alerts, and trigger actions automatically, freeing up valuable human resources for higher-level strategic tasks.
• • You will contribute significantly to Seeq’s customer health scoring framework. This involves developing and refining models that accurately assess the health of customer accounts, providing a standardized measure of success and risk.
• • A key objective is to develop correlation models between product usage and renewal likelihood. By understanding how customers engage with Seeq’s platform, you will help predict which accounts are most likely to renew, allowing for targeted retention efforts.
• • You will be responsible for identifying leading indicators of risk, expansion, and adoption. This proactive approach allows the Customer Success team to intervene before issues escalate or to capitalize on opportunities for growth.
• • Continuous iteration based on measurable predictive lift and feedback from the Customer Success team will be essential. Your work will be evaluated on its tangible impact, and you will be expected to adapt and improve your models and workflows based on real-world results and user input.
• • Embracing modern development practices, you will leverage AI-first coding workflows to accelerate your development process, enhancing productivity and code quality.
• • Where valuable, you will build lightweight LLM-driven synthesis workflows. This involves using large language models to summarize information, generate reports, or extract key insights from unstructured or semi-structured data, adding a layer of advanced intelligence to customer interactions.
• • You will play a role in elevating development practices across the entire Customer Success organization, sharing knowledge and promoting best practices in data analysis and automation.
• • Close partnership with CSMs is vital. You will work directly with them to gain a deep understanding of their real-world decision workflows, challenges, and needs, ensuring your intelligence solutions are practical and impactful.
• • Collaboration with Analytics Engineers on data modeling and structure will ensure that the data foundation is robust and supports the analytical requirements of the role.
• • You will also feed product insights back into the Engineering team through internal dogfooding, providing valuable feedback on product usage and potential areas for improvement based on your analysis of customer data.