Staff Data Scientist
New
J
JobgetherB2B SaaS
Based in United StatesFull-TimeStaff
SalaryBase salary of $164,000–$205,000 for candidates in NYC, the San Francisco Bay Area, and Seattle. Base salary of $171,400–$201,800 for candidates in other U.S. locations. Eligibility for a variable compensation or performance bonus program. Equity participation for full-time employees.
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Job Details
- Experience
- 8+ years of experience in data science, product analytics, data engineering, or a related discipline
- Required Skills
- AWSPythonSQLMachine LearningSnowflakeProduct Analyticsdbt
Requirements
- Have 8+ years of experience in data science, product analytics, data engineering, or a related discipline.
- Have experience in a B2B SaaS or high-growth technology environment.
- Have built data foundations and scalable analytical capabilities in early-stage or low-data-maturity environments.
- Be proficient with SQL, Python, Jupyter notebooks, Snowflake, dbt, Sigma, and AWS.
- Have hands-on experience building production-grade dbt models, transformations, pipelines, tests, documentation, orchestration, and version-controlled workflows.
- Have designed product instrumentation strategies, including event schemas, tracking plans, and reliable data capture.
- Have developed self-service dashboards and visualizations using Sigma, Looker, Tableau, or similar platforms.
- Have expertise in product analytics, including funnel, cohort, retention, behavioral segmentation, feature adoption, and customer lifecycle analysis.
- Have statistical and machine learning expertise, including regression, classification, propensity and churn modeling, clustering, survival analysis, time-to-value analysis, and causal inference.
- Have owned experimentation end to end, including hypothesis development, metrics, randomization, power, duration, analysis, and communicating results.
- Have experience with experimentation platforms such as Statsig or Optimizely, and alternative causal-inference approaches.
- Be able to select analytical methods, validate models, assess uncertainty, and communicate limitations.
Responsibilities
- Define instrumentation requirements, event schemas, data models, and analysis-ready assets with data engineering.
- Translate product data into recommendations through narratives, visualizations, and data storytelling.
- Build self-service dashboards that connect product analytics to OKRs and business outcomes.
- Analyze funnels, retention, cohorts, feature adoption, customer behavior, and usage patterns to inform product direction.
- Establish analytical processes, workflows, documentation, and data quality standards.
- Own experimentation, including hypotheses, metrics, randomization, test duration, power requirements, and analysis plans.
- Apply statistical and machine learning techniques to understand and predict customer behavior.
- Productionize models with data engineering and deliver outputs to product experiences, customer success platforms, CRM systems, and related workflows.
- Monitor model performance, drift, retraining, validation, and model retirement.
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