Senior Data Scientist

About this role

<h3><strong>About AppOmni</strong></h3> <p>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.</p> <p>Recognized as a<a href="https://drive.google.com/drive/folders/1sYC94rvhwD9ARzvgwwaxcs4upkp6acAZ"> <strong><em>Frost Radar™ 2025 Leader</em></strong></a><strong><em> </em></strong><em>and</em><a href="https://www.greatplacetowork.com/certified-company/7078597"><strong><em> Great Place To Work</em></strong></a><strong><em>®</em></strong>, 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.<br><br></p> <h3><strong>About the Role</strong></h3> <p>AppOmni is looking for a Senior Data Scientist to help define and build scalable, production-grade data pipelines and intelligent analytics capabilities within our SaaS platform.</p> <p>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.</p> <p>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.</p> <p>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 technical leadership responsibilities, partnering closely with Product and Engineering to build reliable, scalable, and intelligent data-driven systems</p> <p> </p> <h3><strong>What You’ll Do</strong></h3> <ul> <li>Design and implement scalable batch and real-time data processing systems across large and complex datasets.</li> <li>Build and optimize ETL and streaming data pipelines using modern GCP big data technologies.</li> <li>Support development decisions around model choices, data architecture, data modeling, pipeline orchestration, analytics infrastructure, and production systems.</li> <li>Develop statistical models and analytics capabilities that support product intelligence and operational insights.</li> <li>Design and maintain production-grade data workflows using technologies such as Airflow, Dataflow, PubSub, and PySpark.</li> <li>Contribute across multiple areas of the data ecosystem, including data engineering, monitoring and governance, visualization, and analytics tooling.</li> <li>Establish monitoring, observability, and governance practices for data quality, pipeline reliability, and production health.</li> <li>Partner closely with Engineering to operationalize scalable data infrastructure and analytics systems.</li> <li>Collaborate with Product to shape intelligent, data-driven product capabilities and user experiences.</li> <li>Act as a thought partner across data engineering, analytics, infrastructure, and applied modeling initiatives.</li> <li>Help evolve internal tooling and frameworks that improve scalability, reliability, and operational efficiency across the platform.</li> </ul> <h3><br><strong>What We’re Looking For</strong></h3> <ul> <li>7–10+ years of experience as a Data Scientist, Applied Scientist, Data Engineer, or Machine Learning Engineer, with ownership of production systems.</li> <li>Strong experience building and operating large-scale data pipelines and distributed data processing systems.</li> <li>Hands-on experience within the GCP ecosystem, particularly big data services such as Dataproc, Dataflow, PubSub, and related storage and data lake technologies.</li> <li>Strong proficiency in Python, PySpark, and modern data processing frameworks.</li> <li>Experience working across multiple disciplines of the data stack, including data engineering, analytics, infrastructure, monitoring/governance, APIs, and visualization.</li> <li>Experience with real-time or streaming systems and orchestration frameworks such as Airflow and Apache Beam/Dataflow.</li> <li>Strong foundation in statistical modeling, analytics, and applied data science techniques.</li> <li>Experience designing and maintaining scalable ETL workflows and production data infrastructure.</li> <li>Familiarity with monitoring, observability, governance, and reliability practices for production data systems.</li> <li>Ability to thrive in highly cross-functional environments and contribute across a wide range of technical…