Data Scientist
Build and own data pipelines, statistical models, and dashboards for B2B SaaS metrics across sales, finance, and product teams. Requires strong Python/SQL skills, data quality focus, and end-to-end ownership mindset.
What You'll Do
- Collaborate closely with other teams (Sales, Finance, Product, Marketing, and more) to translate problems and needs into action-oriented data solutions
- Design, build, and maintain data pipelines for reliable ingestion and transformation
- Rapidly prototype and iterate using AI coding tools to accelerate development and reduce toil
- Drive rigor and best practices, with a focus on data quality, consistency, and transparency
- Develop and deploy statistical models and machine learning, where appropriate
- Build clear, decision-oriented visualizations and dashboards for stakeholders across multiple departments
- Own selected production data systems: selection, orchestration, monitoring, and tuning
- Communicate and shepherd key data results and analysis to executives
What We're Looking for
- Experience with B2B SaaS-relevant data, including Salesforce and financial metrics
- Strong communication skills and ability to work effectively across multiple departments and stakeholder groups
- Ownership mindset and ability to deliver end-to-end outcomes independently; must be a "startup type"
- Demonstrated ability to design data pipelines and work with imperfect, evolving data sources
- Sharp attention to data quality, including validation, anomaly detection, and root-cause analysis of inconsistencies
- Strong proficiency in Python and SQL; experience with modern data stack tools (e.g., dbt, Airflow, Spark, or equivalents, a plus)
- Experience with data visualization tools (e.g., Tableau, Looker, or similar)
- Some familiarity with infrastructure and related setup (databases, data warehouses, VMs)
- Knowledge of core machine learning concepts and when to apply them pragmatically
Example Initial Projects
- Build a likelihood-of-close model for Salesforce opportunities, which factors in relevant metadata and history
- Create a framework and initial implementation for an executive operational dashboard, working with a broad set of teams
- Define, validate, and implement key SaaS product-usage metrics
Salary Range: The base pay range for this role is between $170,000 and $190,000. Base pay will depend on your skills, qualifications, experience, and location
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