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Open nowPosted 22 days ago

Cientista de Dados Pl.

PagBank

Where
São Paulo, Brazil
Work mode
Hybrid
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Your applicationOpen nowCientista de Dados Pl.PagBank · São Paulo, Brazil

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on PagBank's own form.

The reply lands in your private mailbox

3×more interviews than doing it yourself with ChatGPT.

The clock on this job

Early applications get read.

12.4% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.4%7 days
  4. 20.0%14 days
  5. 35.5%30 days
  6. 58.6%60 days
This job: posted 22 days ago

The posting

Quando você pensa em carreira, o que vem à sua mente? Propósito? Colaboração? Ser protagonista para fazer a diferença? No PagBank temos tudo isso e muito mais. 💛Estamos em busca de pessoas que vistam a camisa com orgulho. Gente com vontade de facilitar a vida financeira das pessoas e impulsionar negócios!Tá a fim de uma carreira que rende mais? Confira essa oportunidade e #VemProPags!Responsabilidades e atribuiçõesDesenvolvimento de modelos preditivos, ciclo completo com criação de target, features e deploy, para aplicação no contexto de Crédito e Cobrança.Realizar análises exploratórias, segmentações e estudos ad hoc para identificação de oportunidades e mitigação de riscos.Otimização de processos e melhoria contínua dos modelos.Interação com times de engenharia e produtos para garantir a escalabilidade das soluções.Traduzir demandas de negócio em soluções orientadas por dados.Preparar apresentações executivas com recomendações e insights para público técnico e não técnico.Requisitos e qualificaçõesSuperior completo em: Estatística, Matemática, Ciência da Computação, Engenharia e áreas correlatas.Conhecimento de linguagens de programação (estrutura e lógica): Python, R, SQL.Domínio de estatística e ciência de dados aplicada (Modelos de classificação como, Random Forest, XGBoost, LightGBM)e modelos de regressão).Conhecimento em conceitos de criação de book de target no universo de riscos e criação de book de covariáveis (feature Engineer e feature Store).Experiência em estrutura e manipulação de dados em Spark.Conhecimento de gerais de estrutura Cloud (AWS, ou Azure, GCP).Experiência com Github.Conceitos básicos de LLMs (GPT, Claude, Gemini, Llama) e Conhecimento sobre limitações de modelos (alucinações, contexto e viés).Noções de Prompt Engineering. Informações adicionaisPara se destacar nessa posição, seria legal se você também tivesse:Experiência com riscos ou áreas correlatas.

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What happens when you press

One press. We do the rest.

  1. A CV for this posting

    Written against PagBank's own wording, from every piece of relevant proof in your profile.

  2. 25 readers review it

    Recruiter, hiring manager, skeptic and more read every draft, round after round. You get the best round.

    The review screen in CV Rocket: how each CV was read, round by round.

  3. We apply on PagBank's form

    Our application engine gets through the hardest forms there are. Where a question needs you, AI suggests the best answer. Don't want us applying from our IP addresses? Use our Chrome extension: we apply straight from your own browser.

    An application in CV Rocket: every answer filled in on the employer's form.

  4. Every reply, sorted

    PagBank's answer lands in your private mailbox, and we classify it on arrival: interview, question, rejection.

    The CV Rocket inbox: each employer reply classified as an interview, an action or a rejection.

  5. Reply with AI

    AI helps you write the email, checks it and sends it. We show you whether the recruiter read it.

  6. The interview in your calendar

    Full integration with your calendar. The invitation goes straight in.

    An interview invitation in the CV Rocket inbox, added to the candidate's calendar.
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Why it works

3×

more interviews than doing it yourself with ChatGPT.

ChatGPT writes a CV and never learns what happened to it. We see every reply. For each CV we know:

  • How it was written, and how the review scored it
  • When we applied, and how long after the posting went up
  • Which posting, which company, which city
  • Who got the interview, and who heard nothing

That is how we know which CVs get called.

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The numbers game

More applications. More interviews.

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What if my background isn't good enough?

We make the most of the background you have. The CV uses every piece of relevant proof your profile holds, and one of the 25 readers reads your whole profile and flags what the CV left out.

Do you really apply for me?

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