AI Data Scientist

Airtm

Data Scientist developing AI-driven workflows for product and business decisions. Building analytics foundations and dashboards while collaborating with product teams to define metrics and KPIs.

Posted 7/1/2026full-timeRemote • 🇦🇷 ArgentinaJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and deploying AI agent workflows, integrating LLM-based tools, and building analytics foundations using SQL and dbt. Proficient in statistical analysis, dashboard creation, and effective communication of insights to diverse stakeholders.

Highest-signal resume keywords

AI Agent FrameworksSQLPythonTableau Dashboard DevelopmentStatistical Analysis

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

AI Agent FrameworksSQLPythonDbtTableauStatistical AnalysisA/B TestingPrompt EngineeringData AnalysisExperiment Design

Soft Skills

Cross-Functional CollaborationEffective Communication

Tools & Technologies

LangChainLlamaIndexCrewAIBI Tools

Industry Keywords

Data SummarizationInsight GenerationAnalytical QuestionsSuccess MetricsKPI

Tech Stack

Tools & technologies

PythonSQLTableau

About the role

Key responsibilities & impact

Design and deploy AI agent workflows to automate recurring analytical tasks, data summarization, and insight generation pipelines.
Evaluate and integrate LLM-based tools into the data team's workflow, assessing their reliability, accuracy, and fitness for analytical use cases.
Collaborate with product and business teams to define analytical questions, success metrics, and KPIs.
Build and maintain analytics foundation using SQL and dbt, enabling reliable reporting and self-serve analytics.
Design, build, and maintain Tableau dashboards that bring metrics to life and support day-to-day decision-making.
Perform A/B testing and experimentation, including experiment design, statistical inference, significance testing, and result interpretation.
Perform ad-hoc, exploratory, and statistical analyses to uncover insights and validate hypotheses.
Communicate findings clearly to both technical and non-technical stakeholders, translating data into actionable recommendations.
Partner with stakeholders to iterate on metrics, dashboards, and analyses as business needs evolve.

Requirements

What you’ll need

2 to 5 Years of experience in Similar roles
Hands-on experience with AI agent frameworks (e.g., LangChain, LlamaIndex, CrewAI, or similar) and demonstrated ability to build and deploy agentic systems in a production or near-production context.
Proven experience with prompt engineering and evaluating LLM outputs for data-related tasks such as automated reporting, anomaly narration, or natural language querying.
Experience orchestrating multi-step AI pipelines that combine LLMs with structured data sources, APIs, or internal tooling.
Strong SQL and Python skills for data analysis and modeling.
Experience with dbt for analytics engineering workflows.
Experience building dashboards in Tableau (or similar BI tools).
Solid foundation in statistics, experimentation, and hypothesis testing.
Ability to work cross-functionally and communicate insights effectively.

Benefits

Comp & perks

Professional development
Flexible work arrangements