# Data Scientist, North Insights

**Company:** [Cohere](https://hotfix.jobs/companies/cohere)
**Location:** San Francisco, CA, New York, NY
**Role:** Data Science
**Salary:** $150k – $225k/yr
**Skills:** SQL, Python, Git, statistical inference, experimental design, predictive modeling, BigQuery, dbt, Looker, Airflow, small language models
**Posted:** 2026-07-23

> Data Scientist on the Analytics and Data Insights team building agentic analytics, AI impact measurement, customer-facing insights, small language models for data enrichment, experiments, and predictive models that drive product and GTM strategy at Cohere. Requires strong SQL/Python, production modeling experience, statistical expertise, and excitement about AI.

## Job Description

## Responsibilities
- Build bleeding-edge agentic analytics to bring order and clarity to real world data.
- Define AI impact measurement: own end-to-end analytics around adoption, growth, and impact to prioritize features by measured value.
- Ship customer-facing analytics and insights that demonstrate value created for enterprise customers.
- Build small language models to categorize, classify, and enrich message data for visibility into product usage.
- Design and run experiments including A/B tests, causal inference studies, and opportunity sizing for product and go-to-market decisions.
- Build predictive models for forecasting, segmentation, propensity scoring, and opportunity sizing.
- Define analytical priorities, push initiatives from question to production, and own deliverables end-to-end.
- Turn ambiguous business questions into rigorous analytical problems and deliver clear recommendations.
- Collaborate with product, research, sales, and finance teams.

## Requirements
- Strong command of SQL, Python, and Git.
- Expertise in statistical inference, experimental design, and predictive modeling.
- Proven ability to turn ambiguous business questions into rigorous analytical problems with compelling recommendations.
- Experience building and deploying production models (not just notebook analysis), including taking classifiers from prototype to production.
- Genuine excitement about AI; follow the research and have opinions.
- Comfort operating in ambiguity, managing multiple workstreams, and distilling insights into actionable narratives.
- Collaborative style grounded in empathy as much as data.

## Nice-to-Haves
- Familiarity with modern data stack tools such as BigQuery, dbt, Looker, or Airflow.
- Experience with small language models for categorization and classification tasks.

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