Lead Data Scientist

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Lead Data Scientist in the United States.

This role sits at the intersection of advanced data science, large-scale data engineering, and production system design, powering intelligent capabilities within a modern SaaS security platform. You will work on transforming massive, complex datasets into actionable insights, real-time analytics, and customer-facing product features. The position blends hands-on modeling, distributed data systems, and architectural leadership across the full data lifecycle. You will design and operate production-grade pipelines that support both batch and streaming use cases at scale. Working closely with Product and Engineering teams, you will help shape data-driven features that improve security visibility, detection, and decision-making. This is a highly cross-functional and technical role requiring versatility across data engineering, analytics, and applied machine learning. It is ideal for someone who thrives in building robust systems that operate reliably in production environments.

Accountabilities

You will be responsible for designing, building, and scaling end-to-end data systems that power analytics, machine learning, and product intelligence across a complex SaaS platform. This includes ownership of data pipelines, modeling decisions, and production reliability for both batch and real-time workflows.

• Design and implement scalable batch and streaming data pipelines across large-scale datasets.
• Build and optimize ETL workflows and distributed processing systems using modern cloud-based technologies, particularly within the Google Cloud ecosystem.
• Develop statistical models, analytics frameworks, and machine learning capabilities to support product insights and decision-making.
• Lead architectural decisions across data modeling, pipeline orchestration, monitoring, and production systems.
• Build and maintain production-grade workflows using tools such as Airflow, Dataflow, Pub/Sub, and PySpark.
• Establish data governance, observability, and quality monitoring practices across all data systems.
• Partner closely with Engineering and Product teams to deliver scalable, intelligent product capabilities.
• Contribute to the evolution of internal data tooling and infrastructure to improve scalability and operational efficiency.
• Act as a technical leader and mentor across data engineering and applied data science initiatives.

Requirements

This role requires deep expertise across data science, data engineering, and production-scale system design, with strong experience operating in cross-functional, high-complexity environments.

• 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 systems.
• Hands-on expertise with cloud data platforms, particularly Google Cloud services such as Dataflow, Dataproc, and Pub/Sub.
• Strong programming skills in Python and PySpark, with experience in modern data processing frameworks.
• Experience across the full data stack, including analytics, infrastructure, monitoring, governance, APIs, and visualization.
• Strong foundation in statistical modeling, machine learning, and applied data science techniques.
• Experience with real-time and streaming architectures, including orchestration tools like Airflow and Apache Beam.
• Proven ability to design, deploy, and maintain production-grade ETL workflows.
• Strong understanding of observability, reliability engineering, and data quality frameworks.
• Excellent communication skills and ability to collaborate with Product, Engineering, and cross-functional stakeholders.
• Demonstrated versatility across multiple data domains, from infrastructure to applied modeling.

Benefits

• Competitive base salary range of $210,000 - $240,000 USD, depending on experience and location
• Equity package through stock options, enabling long-term participation in company growth
• Comprehensive medical, dental, and vision insurance plans with HSA options
• 401(k) and Roth retirement savings plans
• Generous paid time off, paid holidays, and flexible time-off policies
• Paid parental leave and family-related leave support
• Life insurance, disability coverage, and employee assistance programs
• Monthly wellness reimbursement and mental health support benefits
• Inclusive, collaborative, and innovation-driven work culture.

How Jobgether Works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.