Lead Data Scientist
New
A
AppOmniSaaS security
Remote - USAFull-TimeLead
Salary210,000 - 240,000 USD per year
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Job Details
- Experience
- 7–10+ years
- Required Skills
- PythonETLGCPMachine LearningAirflowData sciencePySpark
Requirements
- 7–10+ years of experience as a Data Scientist, Applied Scientist, Data Engineer, or Machine Learning Engineer.
- Strong experience building and operating large-scale data pipelines and distributed data processing systems.
- Hands-on experience within the GCP ecosystem (Dataproc, Dataflow, PubSub).
- Strong proficiency in Python, PySpark, and modern data processing frameworks.
- Experience working across multiple disciplines of the data stack.
- 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.
- Strong written and verbal communication skills.
Responsibilities
- 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.
- 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.
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