Cohere

Data Scientist, North Insights

Cohere2 days ago
Location

United States

Type

Full Time

Salary

USD 150,000 – 225,000

Level

Mid

Role

Data Scientist

Posted

Jul 23, 2026

Full TimeMid

The role

Summary

As a Data Scientist on Cohere's Analytics and Data Insights team, you will own end-to-end analytical work driving product strategy and enterprise customer value for a leading security-first enterprise AI company. This role involves building agentic analytics infrastructure, defining AI impact measurement frameworks, designing experiments, deploying production models, and shipping customer-facing insights that directly influence go-to-market decisions. You'll need strong expertise in SQL, Python, statistical inference, and experimental design, combined with the ability to translate ambiguous business questions into rigorous analytical problems and drive solutions from prototype to production deployment.

What you'll do

Build Agentic Analytics Infrastructure: Design and develop cutting-edge agentic analytics systems that bring order and clarity to real-world data. Work on solving unsolved problems in autonomous analytics, creating tools that enable data-driven decision-making across the organization. This involves researching emerging patterns in agentic systems and implementing novel approaches to analytics automation.
Define AI Impact Measurement Framework: Own the comprehensive analytics story around product adoption, growth, and business impact that leadership uses to prioritize features based on measured value rather than subjective feedback. Build dashboards and metrics that track how foundational AI models are delivering tangible business outcomes for enterprise customers.
Design and Execute Experiments: Conduct A/B tests, causal inference studies, and opportunity sizing analyses that directly inform product development and go-to-market strategy. Apply rigorous statistical methodologies to validate hypotheses and reduce uncertainty in strategic business decisions across product and sales teams.
Build Production-Grade Predictive Models: Develop and deploy predictive models for forecasting, customer segmentation, propensity scoring, and opportunity sizing across Cohere's core business lines. Ensure models are optimized for production environments, scalable, and deliver measurable business impact beyond prototyping phases.
Ship Customer-Facing Analytics Solutions: Create insights, tools, and analytical products that enterprise customers recognize as evidence of value and ROI from their Cohere investment. Translate complex analytical findings into compelling narratives that demonstrate how foundational AI impacts customer business outcomes.
Build Small Language Models for Data Enrichment: Develop and deploy small language models to categorize, classify, and enrich message data, providing enterprise customers with visibility into team usage patterns and product adoption metrics within their organizations.
Own End-to-End Analytical Initiatives: Take full ownership of analytical projects from problem framing and requirement gathering through model development, validation, production deployment, and ongoing optimization. Define analytical priorities within your scope, manage stakeholder expectations, and drive accountability for results.
Translate Business Questions into Analytical Problems: Demonstrate exceptional ability to take ambiguous, high-level business questions from product, sales, finance, and research teams and convert them into rigorous analytical problems with clear, data-driven recommendations. Communicate findings in concise, actionable narratives tailored to different stakeholder audiences.

What we look for

Technical

SQL ExpertiseAdvanced proficiency in writing complex SQL queries for data extraction, transformation, and analysis. Experience optimizing queries for performance on large-scale enterprise datasets and working with modern data warehouses like BigQuery.
Python ProgrammingStrong Python skills for data manipulation, statistical analysis, and machine learning model development. Proficiency with pandas, scikit-learn, numpy, and other data science libraries. Experience writing production-quality, maintainable code.
Statistical Inference and Experimental DesignDeep understanding of statistical foundations including hypothesis testing, confidence intervals, power analysis, and significance testing. Expertise in designing rigorous experiments including A/B tests, multivariate testing, and causal inference methodologies.
Predictive Modeling and Machine LearningHands-on experience building, validating, and deploying supervised learning models including classification, regression, and forecasting. Understanding of model evaluation metrics, cross-validation, hyperparameter tuning, and avoiding common pitfalls like data leakage.
Git Version ControlProficiency with Git for code collaboration, version control, and maintaining reproducible analytical workflows. Experience working in team environments with branching, pull requests, and code review processes.
Production Model DeploymentDemonstrated ability to take machine learning models from notebook prototypes to production systems handling real-world data at scale. Experience with model serving, monitoring, and maintenance in production environments.

Education

Bachelor's Degree in Quantitative FieldBachelor's degree in Data Science, Statistics, Mathematics, Physics, Computer Science, Economics, or related quantitative discipline. Advanced degree (Master's or PhD) in related field is a plus.

Experience

3-5 Years Data Science ExperienceMinimum 3-5 years of professional experience as a Data Scientist, Analytics Engineer, or similar role involving statistical modeling, experimental design, and business analytics in production environments.
Enterprise Analytics ExperienceExperience working in B2B enterprise environments, preferably in SaaS or technology companies. Understanding of enterprise customer needs, go-to-market considerations, and business metrics that drive enterprise software adoption.
Cross-Functional CollaborationProven track record collaborating effectively with product managers, engineers, research scientists, sales teams, and finance stakeholders. Ability to translate between technical and non-technical audiences and build trust across organizational functions.
Ambiguity ManagementStrong ability to operate effectively in ambiguous environments with incomplete information. Experience taking ownership of problems without clear solutions, asking clarifying questions, and defining success metrics independently.

Skills

Required skills

SQLAdvanced SQL for complex data querying, transformation, and aggregation across large enterprise datasets
PythonProduction-grade Python programming including pandas, scikit-learn, numpy for data analysis and modeling
Statistical InferenceHypothesis testing, confidence intervals, power analysis, and rigorous statistical methodology
Experimental DesignA/B testing, multivariate testing, and designing rigorous experiments that drive product decisions
Predictive ModelingBuilding, validating, and deploying classification, regression, and forecasting models in production
GitVersion control and collaborative development practices using Git
Business AcumenAbility to translate business questions into analytical problems and communicate findings to non-technical stakeholders

Nice to have

BigQueryExperience with Google BigQuery for large-scale data warehousing and analysis
dbt (Data Build Tool)Proficiency with dbt for analytics engineering, data transformation, and data modeling
LookerExperience building dashboards and data visualizations in Looker for stakeholder reporting
Apache AirflowKnowledge of Airflow for workflow orchestration and data pipeline automation
Large Language ModelsPractical experience working with LLMs, prompting strategies, and fine-tuning techniques
Causal InferenceUnderstanding of causal inference methodologies beyond traditional statistical testing
Time Series AnalysisExperience with forecasting, trend analysis, and temporal data modeling
AI ResearchFollowing current AI research, understanding of foundation models, and ability to apply research insights to business problems

Compensation & benefits

Salary

USD 150,000 – 225,000 (annual)

Stock options

Available

Benefits

Weekly Lunch Stipend

Receive $75 (or equivalent in local currency) weekly to support your lunch arrangements, whether in office or remote

Comprehensive Health and Dental Coverage

Full health insurance and dental benefits with a dedicated mental health budget to support your overall wellness

Retirement Savings Programs

RRSP matching for Canadian employees, 401(k) for US-based staff, and pension schemes for UK and European employees

Parental Leave Top-up

Receive 100% salary continuation for up to 6 months of parental leave for either parent, supporting work-life balance during major life transitions

Annual Enrichment Benefits

Dedicated budget for arts and culture, fitness and wellness programs, quality time activities, and workspace improvement to enhance your personal and professional development

Professional Development

Education and learning stipend covering conferences, online courses, coaching, and skill development opportunities

Generous Paid Vacation

6 weeks of paid vacation annually (30 working days), significantly above industry average to ensure adequate time for rest and personal pursuits

Office Travel and Offsite Budget

For remote employees, receive budget to travel to other Cohere offices, plus funding for annual company offsites and team building events

Home Office Setup Allowance

Receive $500 one-time stipend to set up your home workspace with proper ergonomic furniture and equipment

Remote-Friendly Work Environment

Work from anywhere with flexible arrangements. If near an office, enjoy daily lunch programs, snacks, and community events. If remote, access co-working benefits in your city

Global Office Network

Collaborate with teams across Cohere's offices in Toronto, San Francisco, New York City, London, Paris, Montreal, Seoul, and Germany with the flexibility to work remotely

Interview process

  1. 1
    Initial Application Review — Your resume and cover letter are reviewed by Cohere recruiters. The company uses AI-enabled tools to help identify qualified candidates against role criteria, though all applications are considered by human recruiters.
  2. 2
    Recruiter Screening Call — A 30-minute introductory call with a recruiter to discuss your background, interest in the role, and alignment with Cohere's mission and culture
  3. 3
    Technical Assessment or Case Study — Demonstrate your analytical capabilities through either a technical assessment involving SQL and Python work, or a data science case study reflecting real problems the Analytics and Data Insights team tackles
  4. 4
    Hiring Manager Interview — Meet with your potential manager to discuss your experience with end-to-end analytical projects, your approach to translating business questions into rigorous problems, and your collaboration style with cross-functional teams
  5. 5
    Cross-Functional Panel Interview — Interview with stakeholders from product, engineering, or research to assess collaboration abilities, communication skills, and how you'd work together to solve ambiguous problems
  6. 6
    Final Round and Offer — Meet with senior leadership for final-stage discussions about role expectations, long-term growth, and cultural fit at Cohere
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Cohere

Cohere

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Cohere is a leading enterprise AI company focused on building powerful large language models (LLMs) and tools for businesses. The company's mission is to empower enterprises to integrate cutting-edge AI capabilities into their products and operations, making AI accessible and practical for real-world applications. Cohere offers a range of APIs that allow developers and organizations to harness generative AI for tasks such as text generation, summarization, intelligent search, and conversation. They operate within the rapidly expanding artificial intelligence software market, serving a diverse global clientele.

Toronto, CanadaFounded 2019cohere.com

Tech Stack

Languages
PythonSQL
Frameworks
scikit-learnpandasNumPy
Databases
BigQuery
Tools
dbtLookerApache AirflowGit
Other
A/B Testing FrameworksCausal Inference MethodsModel Deployment Pipelines
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