Tecton
Sales Operations Manager
Remote | $166K - $238K
- Description: • Increase quarterly operations project completion rate from ~70% to ~90% • Design, implement, and operationalize Tecton’s post-sales process • Build detailed post-sales reporting and dashboards • Formulate a detailed customer health methodology • Improve current use case process and reporting • Iterate and improve upon consumption tracking and data-driven segmentation • Own and manage repository of customer value stories • Craft Tecton’s Quick Start Solution package • Be a strategic partner to Ryan A. to support weekly/bi-weekly/monthly cadences and short/long-range planning • Build operational resiliency and support pre-sales execution efforts Requirements: • Self-starter • Excellent communication skills • Extreme ownership • Work ethic • Adaptability • Role mastery • Systems knowledge • Data-driven problem solver Benefits:x • Medical, dental, vision, life, 401(K) • Flexible paid time off • 10 paid holidays each calendar year • Sick time • Leave of absence as per... the FMLA and other relevant leave laws
- 51-200 employees
- Software, Technology, Artificial Intelligence, Software Development, Machine Learning, Data Analytics, Cloud Computing, Computer Software, Information Technology and Services, Data Management
- Privately Held
- Founded: 2019
- Last round: Series C
- Last round: US$ 100.0M
- San Francisco, California
- Company Specialties:
Machine Learning, Data Science, Feature Store, Data Engineering, Artificial Intelligence , Big Data, MLOps, DevOps, and Data Platform
Company information
Founded by the team that created the Uber Michelangelo platform, Tecton provides an enterprise-ready feature platform to make world-class machine learning accessible to every company. Machine learning creates new opportunities to generate more value than ever before from data. Companies can now build ML-driven applications to automate decisions at machine speed, deliver magical customer experiences, and re-invent business processes. But ML models will only ever be as good as the data that is fed to them. Today, it’s incredibly hard to build and manage ML data. Most companies don’t have access to the advanced ML data infrastructure that is used by the internet giants. So ML teams spend the majority of their time building custom features and bespoke data pipelines, and most models never make it to production. We believe that companies need a new kind of data platform built for the unique requirements of ML. Our goal is to enable ML teams to build great features, serve them to production quickly and reliably, and do it at scale. By getting the data layer for ML right, companies can get better models to production faster to drive real business outcomes.
