AppOmni
SaaS security
LeadDataScientist
Neural analysis suggests this role is
optimal for Lead candidates.
“Lead Data Scientist at AppOmni. Skills: Data Pipelines, Machine Learning, Statistical Modeling, Data Engineering. Design and implement scalable batch and real-time data processing systems. Build and optimize ETL and streaming data pipelines”
What You'll Achieve.
prevent SaaS data breaches; deliver end-to-end SaaS security; clear visibility into posture, access, third-party connections, AI-related activity; identify unsanctioned SaaS and Shadow AI tools; identify and resolve risks early; keeping their SaaS applications secure; set the standard for innovation and customer value; build scalable, production-grade data pipelines; build intelligent analytics capabilities; transform complex datasets into actionable product insights; customer-facing capabilities; build reliable, scalable, and intelligent data-driven systems; improve scalability, reliability, and operational efficiency
Industry & Context.
What They're Looking For.
Must Have
7–10+ years of experience, Data Scientist, Applied Scientist, Data Engineer, Machine Learning Engineer, ownership of production systems, building and operating large-scale data pipelines, distributed data processing systems, Python, PySpark, modern data processing frameworks, statistical modeling, analytics, applied data science techniques, designing and maintaining scalable ETL workflows, production data infrastructure, written and verbal communication skills
Nice to Have
GCP ecosystem, Dataproc, Dataflow, PubSub, storage and data lake technologies, real-time or streaming systems, orchestration frameworks, Airflow, Apache Beam, monitoring, observability, governance, and reliability practices, versatility, data engineering, infrastructure, analytics applications, statistical modeling, operational production systems
What You'll Do.
Design and implement scalable batch and real-time data processing systems
Build and optimize ETL and streaming data pipelines
Lead development decisions around model choices
Develop statistical models and analytics capabilities
Design and maintain production-grade data workflows
Contribute across multiple areas of the data ecosystem
and governance practices
Partner closely with Engineering to operationalize systems
Collaborate with Product to shape data-driven capabilities
Act as a technical leader and thought partner
Help evolve internal tooling and frameworks
How You'll Work.
Team & Collaboration
Partnering closely with Product and Engineering; Collaborate with Product to shape intelligent, data-driven product capabilities; Act as a technical leader and thought partner across data engineering, analytics, infrastructure, and applied modeling initiatives; highly cross-functional environments
Communication Scope
written and verbal communication skills
Full Job Description
About AppOmni AppOmni prevents SaaS data breaches by delivering end-to-end SaaS security. Our platform gives security teams clear visibility into posture, access, third-party connections, AI-related activity, and with built-in discovery to identify unsanctioned SaaS and Shadow AI tools. Backed by continuous monitoring and real-time threat detection, AppOmni helps enterprises identify and resolve risks early, keeping their SaaS applications secure. Recognized as a Frost Radar™ 2025 Leader and Great Place To Work®, AppOmni continues to set the standard for innovation and customer value in SaaS security. The largest and fastest-growing global enterprises across industries trust AppOmni to secure their SaaS applications. About the Role AppOmni is looking for a Lead Data Scientist to help define and build scalable, production-grade data pipelines and intelligent analytics capabilities within our SaaS platform. In this role, you will apply data science, statistical modeling, batch and real-time analytics, and large-scale data engineering to transform complex datasets into actionable product insights and customer-facing capabilities. You will work across a broad range of technical domains on pipelines, including ETL, statistical modeling, machine learning (supervised and unsupervised) and LLM as well as monitoring, governance, visualization, and production modeling systems. We are looking for a highly versatile engineer-scientist — someone who has worked across different layers of the modern data stack and enjoys continuing to solve a wide variety of technical problems. This role is ideal for someone whose background spans data engineering, infrastructure, analytics applications, statistical modeling, and operational production systems. You will be responsible for end-to-end data workflows, from ingestion and transformation through analytics implementation, orchestration, monitoring, governance, and production operations. This is a hands-on individual contributor role with
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