AppOmni

Technical Account Manager

• Manage and serve as the primary point of contact for all post-sales technical and functional needs for AppOmni’s top enterprise customers through strong technical acumen, in-depth product knowledge, relationship-building, strategic planning, business value articulation, and execution.
• Be consultative and be viewed as a technical SME by exhibiting a complete understanding of customer technical goals and objectives. Develop a trusted advisor relationship with customer champions, sponsors, and technical teams to drive product adoption and ensure they are using AppOmni to achieve full business value.
• Ensure that a deployment and adoption plan is in place with each customer in order to facilitate the customer achieving their stated success criteria. Work closely with AppOmni’s Customer Success Manager (CSM) team to monitor and identify adoption and utilization trends, provide recommendations based on customers' needs, risks, and strategy.
• Introduce, demonstrate, and implement new products and features as they become available.
• Develop expertise in AppOmni’s API capabilities to help customers augment UI-based functionality with script-based approaches to unlock value.
• Work closely with the Product and Engineering teams to define and influence SaaS application support roadmap and augmentation beyond the AppOmni Developer Platform.
• Increase customer retention by assisting regular health check meetings for tactical items, and assisting with strategic business reviews for alignment of objectives and outcomes
• Help the customer document their technical success criteria and strategy and partner with the CSM and customer teams to ensure adherence to the strategy; work with the customer to update technical success criteria if/as customer priorities shift.
• Define and document the customer’s end-state AppOmni architecture and work with the customer to develop a roadmap and strategy for achieving the desired end-state architecture.
• Track accounts to identify customer risk and work actively to eliminate that risk; partner with the CSM by contributing to the renewal strategy and work closely with the AppOmni Renewals Manager to influence the successful execution of renewals.
• Be the technical voice of the customer to our internal stakeholders (engineering, product, sales, and executive leadership teams) and provide internal feedback on how AppOmni can better serve our enterprise customers.
• Work closely with Product and Engineering on identification and tracking of product improvement requests, troubleshooting, and bugs.
• Evangelize customer success stories with the AppOmni Marketing team.
• Above all, put customer’s needs first and demonstrate customer obsession.

Data Scientist

• Design and implement scalable batch and real-time data processing systems across large and complex datasets.
• Build and optimize ETL and streaming data pipelines using modern GCP big data technologies.
• Lead development decisions around model choices, data architecture, data modeling, pipeline orchestration, analytics infrastructure, and production systems.
• Develop statistical models and analytics capabilities that support product intelligence and operational insights.
• Design and maintain production-grade data workflows using technologies such as Airflow, Dataflow, PubSub, and PySpark.
• Contribute across multiple areas of the data ecosystem, including data engineering, monitoring and governance, visualization, and analytics tooling.
• Establish monitoring, observability, and governance practices for data quality, pipeline reliability, and production health.
• Partner closely with Engineering to operationalize scalable data infrastructure and analytics systems.
• Collaborate with Product to shape intelligent, data-driven product capabilities and user experiences.
• Act as a technical leader and thought partner across data engineering, analytics, infrastructure, and applied modeling initiatives.
• Help evolve internal tooling and frameworks that improve scalability, reliability, and operational efficiency across the platform.

Lead Data Scientist

Security Engineer

• Design and develop detection logic and security rules to identify threats, suspicious behaviors, and misconfigurations across SaaS applications.
• Research SaaS platforms (e.g., Google Workspace, Microsoft 365, Salesforce, Slack, etc.) to understand security models, APIs, and potential attack surfaces.
• Translate real-world attack techniques and SaaS security risks into scalable product capabilities, including detections, posture checks, and risk signals.
• Contribute to both threat detection and posture management content, ensuring broad coverage across identity, access, integrations, and data exposure risks.
• Analyze large-scale SaaS telemetry to identify patterns, anomalies, and opportunities for new detections or improvements.
• Continuously improve detection quality by reducing false positives and ensuring signals are actionable for customers.
• Collaborate with Engineering to productionize detection logic and ensure reliable execution at scale.
• Partner with Product to shape how security insights are surfaced, prioritized, and explained to users.
• Stay current on emerging SaaS attack techniques, identity threats, OAuth risks, and AI-related security considerations.
• Contribute to internal knowledge sharing and help elevate SaaS security expertise across the organization.

Senior Security Engineer

• Design and develop detection logic and security rules to identify threats, suspicious behaviors, and misconfigurations across SaaS applications
• Research SaaS platforms (e.g., Google Workspace, Microsoft 365, Salesforce, Slack, etc.) to understand security models, APIs, and potential attack surfaces
• Translate real-world attack techniques and SaaS security risks into scalable product capabilities, including detections, posture checks, and risk signals
• Contribute to both threat detection and posture management content, ensuring broad coverage across identity, access, integrations, and data exposure risks
• Analyze large-scale SaaS telemetry to identify patterns, anomalies, and opportunities for new detections or improvements
• Continuously improve detection quality by reducing false positives and ensuring signals are actionable for customers
• Collaborate with Engineering to productionize detection logic and ensure reliable execution at scale
• Partner with Product to shape how security insights are surfaced, prioritized, and explained to users
• Stay current on emerging SaaS attack techniques, identity threats, OAuth risks, and AI-related security considerations
• Contribute to internal knowledge sharing and help elevate SaaS security expertise across the organization

Senior SaaS Security Engineer

Full-stack Engineer

• Leading development efforts, mentoring engineers and product managers, and making key architectural decisions that involve Data Science, Machine Learning and AI.
• Partner with Sales, Marketing, Customer Support and other departments across the organization for a full end to end ownership from the product and technical perspective as well as internal enablement and customer support.
• Develop greenfield projects and implement proof of concepts, including hands-on coding and connection to the product vision.
• Architect end-to-end AI systems by choosing the best AI approach (Data Science, Statistics, Machine Learning and Generative AI) for the problem to be solved, considering all relevant trade-offs and risks, and work hands-on directly in the code with other engineers.
• Bridge the gap between data science and product by translating applicable complex problems into holistic AI solutions, including the UX and UI, and leading its development while also managing from the product management perspective.
• Use data-driven solutions to address complex cybersecurity problems.
• Be responsible for the pipeline metrics and work hands-on with MLOps to optimize model and pipeline performance through proper monitoring, data mining, fine-tuning, prompt engineering, and hyperparameter tuning to meet latency and cost requirements.
• Drive technical strategy by evaluating third-party AI tools versus building in-house solutions to maximize ROI.
• Establish data governance and security standards to ensure the ethical and compliant use of sensitive information within AI models.
• Create and implement incremental development and implementation plans based on a product long term vision.

Senior AI Product Engineer

Customer Success Manager

• Manage and serve as the primary point of contact for all post-sales activity for AppOmni’s top enterprise customers through strong relationship-building, product knowledge, planning and execution
• Be consultative and build in-depth relationships and a complete understanding of customer business goals and objectives. Develop a trusted advisor relationship with customer champions and executive sponsors to drive product adoption and ensure they are using AppOmni to achieve full business value.
• Ensure that a plan is in place with each customer for deployment, change management and adoption programs. Monitor and identify adoption and utilization trends, provide recommendations based on customers' needs, risks, and strategy.
• Introduce and implement new products and features as they become available, with the support of the AppOmni Technical Account Manager and/or Implementation Manager.
• Increase customer retention by conducting regular health check meetings for tactical items, and performing strategic business reviews for alignment of objectives and outcomes
• Track accounts to identify churn risk and work actively to eliminate that risk; own the renewal strategy and work closely with the AppOmni Renewals Manager to influence the successful execution of renewals.
• Be the voice of the customer to our internal stakeholders (engineering, product, sales, and executive leadership teams) and provide internal feedback on how AppOmni can better serve our enterprise customers
• Work closely with Product and Engineering on identification and tracking of product improvement requests, troubleshooting, and bugs
• Simultaneously manage multiple enterprise customers who are at different points in the account lifecycle
• Evangelize customer success stories with the Marketing team
• Above all, put customer’s needs first and demonstrate customer obsession

• Design and implement data-driven risk scoring and prioritization approaches across SaaS security signals.
• Lead the development of AI-powered product capabilities, including agentic and LLM-based features that support investigation, triage, and security operations workflows.
• Define and evolve explainable decision logic so customers understand why issues are prioritized or actions are recommended.
• Contribute to approaches that assess the potential scope and impact of security issues.
• Establish evaluation methods to measure model quality, effectiveness, and reliability over time.
• Incorporate product usage signals and feedback to guide continuous improvement of ML and AI systems.
• Monitor ML and AI systems in production to ensure stability, safety, and consistent behavior.
• Partner with Engineering to operationalize models and AI workflows, supporting safe deployment and iteration.
• Collaborate with Product to shape AI-driven user experiences, ensuring alignment with customer needs and trust expectations.
• Act as a technical leader and thought partner on applied ML and AI across the product area.