Data Scientist

Vaga não está no LinkedIn

🕒 Junho 16

🇲🇽 México – Remoto

💵 $75.000 - $85.000 / ano

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

📊 Cientista de Dados

👻 Score fantasma 13%

O que significa: quão provável esta vaga está aberta sem contratação ativa.Como ler: 60% ou mais significa que achamos que é uma vaga fantasma. Pontuações mais baixas preocupam menos.Como pontuamos: tempo em aberto, republicações, vagas abertas vs tamanho do time, rapidez para fechar vagas, e se o salário está divulgado.Observação: é uma estimativa de dados públicos, não um julgamento da empresa. Vagas reais podem ter pontuação alta, então use como uma dica, não como um fato.

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Traackr

SiteLinkedInTodas as Vagas

51 - 200 funcionários

☁️ SaaS

📣 Marketing

SaaS • Marketing

A Traackr é uma plataforma de software de marketing de influenciadores projetada para profissionais de marketing orientados por dados. A plataforma simplifica o marketing de influenciadores, fornecendo ferramentas para descoberta, gestão de campanhas, colaboração com influenciadores e relatórios. A Traackr foca em ajudar os usuários a fazerem investimentos mais inteligentes, oferecendo um conjunto abrangente de dados globais de influenciadores, avaliação de mercado e orientação especializada. Atende tanto a pequenas equipes quanto a programas complexos, sendo utilizada por grandes marcas para otimizar estratégias e medir o retorno sobre o investimento.

Descrição

• Partner with Product and Engineering to identify high-impact opportunities, frame ambiguous problems, define success metrics, and choose pragmatic approaches (heuristics, statistics, ML, or GenAI). • Lead rigorous experimentation across teams: hypothesis design, metric/guardrail definition, power analysis, A/B testing (or quasi-experiments), and clear readouts that drive decisions. • Build and iterate on ML/AI capabilities that ship to production (e.g., classification, information extraction, ranking/recommendations, and GenAI components such as RAG or developing the agent harnesses for our core agentic journeys), optimizing for value-added, latency, and cost. • Establish best-in-class evaluation practices for both ML and LLM features: golden datasets, offline/online evaluation plans, regression suites, and monitoring that catches quality drift early. • Enable engineers to build safely and effectively with AI by coaching on prompt patterns, tool/function calling, structured outputs, guardrails, and debugging/evaluation workflows. • Design and support agentic workflows where they add real product value, with clear constraints, observability, and fallbacks. • Support the end-to-end lifecycle of deployed models and AI systems: data requirements, training/fine-tuning where relevant, validation, deployment, monitoring, incident response, and continuous improvement. • Raise org-wide leverage by creating reusable assets (evaluation harnesses, shared datasets, templates, documentation) and running enablement workshops. • Communicate insights and tradeoffs clearly to technical and non-technical stakeholders, turning analyses into decisions and measurable impact. • Champion responsible, privacy-aware AI: appropriate data handling, bias/fairness considerations where applicable, and human-in-the-loop workflows when needed.

🎯 Requisitos

• 3+ years (or equivalent) delivering data science work that shipped to production and/or materially influenced product direction • Experience collaborating cross-functionally and communicating clearly with diverse stakeholders; ability to influence without authority • Strong Python and SQL skills, with the ability to write maintainable, production-quality code (testing, reviews, documentation) • Demonstrated mentorship/enablement—helping other engineers and teams adopt best practices and ship faster with higher quality • At least 3 of: • Strong applied statistics and experimentation skills (A/B testing, causal thinking, metric design, interpretation under uncertainty) • Proven ability to evaluate and improve models in real conditions: dataset design, error analysis, offline metrics, online measurement, monitoring, and iteration • Hands-on experience building with LLMs in product contexts, including some of: RAG/grounding, tool/function calling, structured outputs, prompt iteration, quality/cost/latency tradeoffs • Practical approach to LLM evaluation: golden sets, regression testing, human review loops, and monitoring for quality drift • Experience with modern MLOps/LLMOps practices (experiment tracking, ETL pipelines, versioning, CI/CD for ML, observability) • NLP and information extraction/classification on noisy social/content data • Experience developing and evaluating large scale Retrieval, Recommendation- and Search Systems.

🏖️ Benefícios

• Competitive Salary • Remote Work Options with Hybrid Flexibility and Home Office Set-Up Stipend • Coworking Office Subscription for Collaborative Spaces • Health, Dental, and Life Insurance Coverage* • Open Vacation Policy and Flexible Holiday Schedule to Suit Your Needs • Paid Parental Leave to Support Quality Time with Your Loved Ones • Career Development, including Internal and External Training Opportunities