Senior Data Scientist

hace 2 semanas

Desde casa, México R2 A tiempo completo

**Who we are**:
At R2, we believe that small and medium businesses are the productive engine of society. Small and medium businesses (SMBs) make up over 90% of companies in Latin America, yet they face a trillion-dollar credit gap. Our mission is to unlock SMBs’ potential by providing financial solutions that are tailored to their needs. We are reimagining the financial infrastructure of Latin America - where SMBs financial needs are satisfied without ever having to go to a bank.

R2 enables platforms in Latin America to embed financial services that SMBs can then leverage at the point of need and within the operating tools they use. We are a tight knit team coming from organizations such as Google, Amazon, Nubank, Uber, Mercado Libre, J.P. Morgan, Capital One and Ualá. We are backed by world-class technology investors such as Gradient Ventures (Google’s AI-focused fund), General Catalyst, and Y Combinator.

To learn more about R2, here are a few articles that have been published about recently: Fintech R2 Inks $100 Million Credit Line to Boost Mexico Lending, Google leads fresh investment in embedded lending fintech R2, and Rappi and R2 to Provide Loans to Restaurants in Mexico and Colombia.

We are a data-first company — data is the core of our product and the lifeblood for all of our decision-making. As a **Senior Data Scientist **you will sit at the helm of R2 by analyzing in a descriptive, predictive, and prescriptive way large data from some of the leading technology platforms in the world, and deploying clever and scalable data-driven solutions that enable new financial opportunities to millions of small businesses across Latin America. Your solutions will drive critical business decisions, such as who to fund and how much to offer, in an automated and scalable way.

**What you’ll work on**:

- Building data structuring, cleaning, and enriching processes from a wide variety of data sources.
- Developing descriptive and insight-oriented analyses of merchant and transaction-based data.
- Process transaction-level data with various approaches, including feature-based and time-series, approaches.
- Stating hypotheses and testing them using basic statistical tests.
- Building regression, classification and forecasting models, mostly aligned with the credit risk capability.
- Developing, deploying and monitoring frameworks for machine learning models at scale.

**Who you are**:

- You have 4 years of experience with data science in theoretical and practical levels.
- You have strong programming abilities (structured, object-oriented, and/or event-oriented programming) and are very comfortable programming in Python and SQL.
- You have strong statistical and mathematical knowledge, including linear algebra, calculus, and statistical inference, among other topics.
- You have built regression and/or classification models in the past for solving financial problems.
- You are comfortable consuming data through APIs, SFTP, or straight-up CSVs.
- You have deployed machine learning models in cloud architectures (AWS preferably), following MLOps best practices and defining training, testing and monitoring stages with re-training loops.
- You have an owner mindset: you care about getting to the bottom of how to make a startup successful.
- You understand every problem as an opportunity for analyzing and learning.
- Have a notable stakeholder management experience, keeping everyone up-to-date with key findings and explaining in a non-technical way results, methodologies and processes for data-driven decision-making.

**Bonus Points**:

- Know how to implement machine learning pipelines, from data acquisition to model deployment and monitoring.
- Have worked with unsupervised learning techniques, such as clustering and/or dimensionality reduction.
- Have built in the past dashboards to show KPIs and analytics results.

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