Lead Data Scientist

AppOmni

Lead Data Scientist developing scalable data pipelines and analytics capabilities at AppOmni. Focus on transforming complex datasets into actionable insights within our SaaS platform.

Posted 5/29/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $210,000 - $240,000 per yearWebsite

Tech Stack

Tools & technologies

AirflowApacheETLGoogle Cloud PlatformPySparkPython

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

7–10+ years of experience as a Data Scientist, Applied Scientist, Data Engineer, or Machine Learning Engineer, with ownership of production systems.
Strong experience building and operating large-scale data pipelines and distributed data processing systems.
Hands-on experience within the GCP ecosystem, particularly big data services such as Dataproc, Dataflow, PubSub, and related storage and data lake technologies.
Strong proficiency in Python, PySpark, and modern data processing frameworks.
Experience working across multiple disciplines of the data stack, including data engineering, analytics, infrastructure, monitoring/governance, APIs, and visualization.
Experience with real-time or streaming systems and orchestration frameworks such as Airflow and Apache Beam/Dataflow.
Strong foundation in statistical modeling, analytics, and applied data science techniques.
Experience designing and maintaining scalable ETL workflows and production data infrastructure.
Familiarity with monitoring, observability, governance, and reliability practices for production data systems.
Ability to thrive in highly cross-functional environments and contribute across a wide range of technical challenges.
Demonstrated versatility — a background that spans multiple types of data applications, infrastructure, and analytics work is highly valued.
Experience partnering closely with Product and Engineering to deliver customer-facing capabilities.
Strong written and verbal communication skills.

Benefits

Comp & perks

Generous paid time off
Paid company holidays
Paid floating holidays
Paid parental leave
Paid sick time and paid family leave for applicable states
Health insurance - medical, dental, and vision with HSA option
LifeWorks Employee Assistance Program
Company-provided life insurance
AD&D, STD/LTD and additional supplemental life insurance options
401(k) and Roth retirement saving accounts
Monthly wellness benefit reimbursement

ATS Keywords

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Hard Skills & Tools

data processingETLstatistical modelingdata architecturedata modelingpipeline orchestrationanalytics infrastructurePythonPySparkbig data technologies

Soft Skills

leadershipcollaborationcommunicationcross-functional teamworkproblem-solvingversatilityownershiptechnical leadershipthought partnershipoperational efficiency