[All remote jobs](https://jobicy.com/jobs.md)[![Airtm logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/1994f28e-221.png)](https://jobicy.com/company/airtm.md)[Airtm](https://jobicy.com/company/airtm.md) # AI Data Scientist Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/airtm.md)ShareRemote from[LATAM](https://jobicy.com/job-region/latam.md)SalaryUndisclosedDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceOpen levelPublished2 Oct 2026Apply before1 Nov 2026Listing views51Application actions5Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Airtm is seeking a senior Data Scientist to strengthen product and business decisions through analytics, experimentation, and AI-enabled data workflows. The role combines analytics engineering, dashboard ownership, statistical analysis, and stakeholder-facing insight delivery. A major focus is designing and deploying agentic LLM workflows for recurring analysis, reporting, and data-maintenance tasks. The successful candidate will use SQL, Python, dbt, Tableau, and experimentation methods to deliver reliable self-service analytics for a financial-infrastructure platform. This is a LATAM-based full-time role requiring at least five years of related experience and strong cross-functional communication. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 4/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a senior, hybrid data science and analytics engineering position requiring production-minded AI-agent implementation alongside strong statistical, SQL, dbt, and Tableau expertise. The role also demands independent ownership of data reliability and clear communication of complex findings to varied stakeholders. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianMarket rate$145,000US market range$120k–$175k0$193k AI insightNo actual compensation is disclosed in the posting, so these figures are estimates for the US market in USD. For a senior Data Scientist with 5+ years of experience spanning analytics engineering, experimentation, Tableau, dbt, SQL/Python, and production-oriented LLM agent workflows, an estimated annual market range is $120,000 to $175,000, with a midpoint of $145,000. Actual compensation may differ substantially for a LATAM-based hire depending on country, employment structure, and local market practices. ## Core skills Skills and capabilities most closely associated with this opportunity. [Data Science](https://jobicy.com/jobs?search_keywords=Data%20Science.md)[Artificial Intelligence](https://jobicy.com/jobs?search_keywords=Artificial%20Intelligence.md)[LLM Agents](https://jobicy.com/jobs?search_keywords=LLM%20Agents.md)[LangChain](https://jobicy.com/jobs?search_keywords=LangChain.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[dbt](https://jobicy.com/jobs?search_keywords=dbt.md)[Tableau](https://jobicy.com/jobs?search_keywords=Tableau.md)[A/B Testing](https://jobicy.com/jobs?search_keywords=AB%20Testing.md)[Statistical Analysis](https://jobicy.com/jobs?search_keywords=Statistical%20Analysis.md) Sample interview questionsHow would you design an AI agent workflow to automate recurring business-performance analysis?I would first define the recurring decisions, metrics, source tables, acceptable output format, and required human-review points. I would build a modular workflow that retrieves governed data, runs deterministic metric calculations, uses an LLM only for interpretation and narrative generation, and logs prompts, inputs, outputs, and evaluations. I would then validate performance against a curated benchmark set and monitor accuracy, cost, latency, and failure modes in production. How do you ensure Tableau dashboards remain accurate and dependable for daily decisions? I establish clear metric definitions, test transformations in dbt, add freshness and quality checks, and document lineage from source data to dashboard fields. I also monitor refresh status, reconcile key dashboard metrics with source queries, and prioritize changes through version control and stakeholder sign-off. Regular maintenance includes reviewing broken filters, performance bottlenecks, and unexpected shifts in metric behavior. Describe your approach to designing an A/B test for a product feature. I begin by defining the decision, primary metric, guardrail metrics, target population, and expected effect size. I calculate sample-size and duration requirements, ensure randomization is valid, and predefine exclusion rules and the statistical method before launch. Afterward, I assess statistical significance, practical impact, data-quality issues, segment effects, and whether the result supports rollout, iteration, or further testing. How would you evaluate whether an LLM is reliable enough to generate automated data insights? I would create a representative evaluation dataset containing expected insights, edge cases, ambiguous inputs, and known failure scenarios. Evaluation would measure factual grounding in structured data, numerical accuracy, completeness, consistency, harmful hallucinations, and usefulness to intended users. I would use deterministic calculations as the source of truth, constrain the model with retrieved context, and require human review for high-impact recommendations. How do you translate an exploratory analysis into a recommendation for non-technical stakeholders? I start with the business question and lead with the decision-relevant conclusion rather than methodology. I explain the size and confidence of the effect in plain language, use a focused visualization, state material limitations, and recommend a specific next action. I provide supporting technical detail separately so stakeholders can act quickly while technical partners can validate the analysis. Opportunity details ## About this role. About us: Airtm is a financial-infrastructure company building the future of the online-work economy. We are on a mission to empower the world’s growing number of Digital Entrepreneurs in the Global South, giving them the financial freedom to thrive. The problem is clear: in emerging markets, accessing the dollar economy is difficult. Cross-border payments are slow, expensive, and often lose value to inflation. This limits the potential of millions of talented individuals. Airtm’s solution is a swift and comprehensive financial platform that facilitates low-value cross-border payments and local cash-outs. As pioneers in stablecoin-payment infrastructure, Airtm has built the most advanced cross-border payment system available on the market. As a company married to the world of online work, Airtm will go beyond payments to build the necessary infrastructure the online-work economy needs to thrive. We are fostering an entirely new economy, giving individuals, communities, and countries the tools to take control of their financial destinies. About the role: We’re looking for a data-driven, curious, and collaborative Data Scientist to support product and business decision-making through analytics, experimentation, and applied data science. In this role, you will be responsible for defining new metrics, validating data to generate insights, and owning the end-to-end lifecycle of our data infrastructure—from creating and maintaining Tableau dashboards to building and adapting data fields and tables. Furthermore, as AI capabilities reshape how data teams operate, you’ll play an active role in designing and deploying AI-powered agentic workflows to automate these analysis, maintenance, and modeling tasks at scale. Key Responsibilities – 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 perform weekly maintenance of Tableau dashboards to bring metrics to life, ensuring data accuracy and platform stability for 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 design, develop, and iterate on key metrics, interactive dashboards, and data analyses to support evolving business needs. Qualifications -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 – Solid foundation in statistics, experimentation, and hypothesis testing. – Ability to work cross-functionally and communicate insights effectively. Nice to Have – Exposure to cloud platforms (AWS) for data storage or analytics workloads. – Knowledge of feature engineering and model evaluation concepts. – Experience with version control (Git). ### Compensation ### Additional Information Show more [Apply now >](https://jobicy.com/jobs/154413-ai-data-scientist.md) > Annual salary information is not provided for this position. 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