# Senior Data Scientist

**Company:** [Fetch](https://hotfix.jobs/companies/fetch)
**Location:** Remote
**Role:** Data Science
**Experience:** 5+ years
**Skills:** Python, SQL, Snowflake, dbt, Airflow, Spark, AWS, Statistical Modeling, experimental design, Causal Inference, predictive modeling, bayesian methods, kpi measurement, Data Visualization, Machine Learning
**Posted:** 2026-08-14

> Own complex analyses, experimentation, statistical modeling, and KPI frameworks that guide product and business decisions. The role requires 5+ years of data science or applied analytics experience, strong SQL and Python skills, quantitative expertise, and the ability to influence cross-functional stakeholders.

## Job Description

## Responsibilities

### Analytics & Modeling
- Independently design and execute complex analyses, statistical models, and measurement frameworks that inform product and business decisions.
- Apply statistical methods such as experimental design, causal inference, predictive modeling, and Bayesian approaches.
- Translate ambiguous business questions into structured analytical approaches, identifying appropriate metrics, methodologies, and data.
- Balance rigor, speed, precision, and practicality based on the business decision.

### Business Impact & Experimentation
- Own analytics for a product or business area, defining and maintaining core KPIs and measurement frameworks.
- Identify opportunities where analytics and experimentation can improve user behavior, revenue, conversion, retention, cost efficiency, and other outcomes.
- Design and analyze experiments with Product and Engineering partners.
- Connect metric movements and analytical findings to underlying business drivers and recommended actions.
- Quantify the impact of product and business initiatives to influence roadmap and prioritization decisions.

### Collaboration & Influence
- Partner with Product, Engineering, Marketing, and Data Product stakeholders on product and business decisions.
- Communicate complex analyses through clear narratives and visualizations, including assumptions, trade-offs, confidence levels, and expected business impact.
- Translate technical and analytical concepts for non-technical partners.
- Increase data literacy by making metrics, analyses, and recommendations accessible and actionable.
- Informally mentor junior data scientists and analysts.

### Technical Excellence
- Use Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS to conduct and scale analytical work.
- Apply strong practices in experimentation, model validation, reproducibility, and governance.
- Use AI/ML tools thoughtfully to improve analytical workflows, automation, documentation, and anomaly detection while maintaining appropriate validation.

## Requirements

### Minimum Requirements
- 5+ years of experience in data science, machine learning, or applied analytics, with ownership of complex analytical problems in product-driven environments.
- Strong expertise in statistical modeling, experimental design, and causal inference.
- Experience owning KPIs, measurement, or analytics for a product or business area and translating findings into actionable recommendations.
- Ability to structure ambiguous business problems and connect analytical findings to revenue, cost, conversion, retention, or user behavior.
- Strong proficiency in SQL and at least one programming language, preferably Python.
- Experience with large-scale data systems such as Snowflake, dbt, Airflow, or Spark.
- Ability to communicate complex technical insights, trade-offs, and confidence levels to technical and non-technical stakeholders.
- Ability to work independently while navigating cross-functional dependencies and escalating broader trade-offs appropriately.
- Bachelor’s degree in Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field.

### Preferred Requirements
- Advanced degree in a quantitative discipline.
- Experience deploying or operationalizing predictive or statistical models.
- Consumer-product experience using data to understand user behavior and inform engagement, retention, monetization, or other customer outcomes.
- Experience building frameworks that improve experimentation velocity.

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