Data Scientist Sênior - Crédito

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Scientist Sênior - Crédito based in Brazil.

This role is designed for an experienced Data Scientist who wants to apply advanced analytics and machine learning to solve complex credit challenges.

You will be responsible for building, deploying, and improving predictive models that support strategic business decisions and scalable financial products.

The position combines technical depth, business understanding, and collaboration with multidisciplinary teams across credit, product, and operations.

You will work with large-scale datasets, developing solutions that improve efficiency, risk management, and customer experiences.

The role offers the opportunity to contribute to a fast-paced, innovative environment where data-driven decisions create measurable impact.

You will have autonomy to explore new approaches, improve processes, and apply modern Data Science and AI practices.

The position can be performed remotely, offering flexibility while collaborating with teams across Brazil.

Accountabilities

As a Senior Data Scientist focused on credit solutions, you will lead end-to-end Data Science initiatives, from problem discovery and experimentation to production deployment and continuous improvement. You will collaborate closely with business and technical teams to transform data into actionable insights, improve credit operations, and ensure machine learning solutions generate measurable value.

• Partner with Credit teams, including Policy, Analysis, Monitoring, and Collections areas, to identify opportunities and develop data-driven solutions.
• Lead complete Data Science projects, including problem definition, data exploration, model development, experimentation, validation, and production implementation.
• Develop, deploy, monitor, and maintain machine learning models applied to credit-related challenges.
• Perform data drift and concept drift monitoring using appropriate frameworks and methodologies.
• Conduct advanced analyses to identify patterns, trends, and insights that improve credit operations and decision-making.
• Create and maintain metrics, KPIs, reports, and dashboards related to credit products and operational performance.
• Apply best practices in Data Science, software development, and AI-assisted productivity tools to improve delivery quality and efficiency.
• Continuously evaluate new technologies and methodologies that can enhance analytical capabilities and business outcomes.

Requirements

The ideal candidate combines strong technical expertise in Machine Learning, statistical modeling, and large-scale data processing with the ability to translate complex analyses into business impact. You should have experience building production-grade models and collaborating with stakeholders in dynamic environments.

• Solid experience applying Machine Learning techniques and statistical modeling to business problems, including regression, classification, and time series models.
• Experience developing credit models, such as application, behavior, or collection models, with knowledge of credit metrics and KPIs.
• Experience working with credit products, especially solutions involving corporate customers or post-paid credit scenarios.
• Strong proficiency in Python, SQL, and Spark for data manipulation, analysis, and modeling with large datasets.
• Strong knowledge of statistics, statistical inference, and hypothesis testing.
• Experience with code versioning practices using Git.
• Degree or equivalent practical experience in Computer Science, Engineering, Statistics, Mathematics, Physics, or related fields.
• Ability to communicate technical concepts clearly and collaborate effectively with business and technical stakeholders.

Nice To Have

• Experience with Databricks, including Model Serving, Unity Catalog, or Declarative Automation Bundles.
• Experience with receivables anticipation products or financial technology solutions.
• Knowledge of causal inference methods, including power analysis, regression with controls, instrumental variables, or Bayesian approaches.
• Experience building AI agents to automate manual processes and evaluate LLM-based solutions.
• Familiarity with AWS cloud services.
• Experience with MLOps frameworks, especially MLFlow.

Benefits

• Remote work model with flexibility to work from anywhere in Brazil.
• Full-time employment model with an 8-hour workday, Monday through Friday.
• Health and dental insurance without copayment.
• Life insurance and support programs for health and well-being.
• Financial wellness support and access to mental health resources, including therapy or nutrition sessions.
• Flexible meal and food allowance through a benefits card.
• Childcare support and extended parental leave programs.
• Education support covering courses, certifications, language programs, and professional development initiatives.
• Home office assistance, equipment support, and coworking partnerships.
• Birthday day off, referral bonuses, annual performance-based incentives, and stock option opportunities.
• Collaborative, innovative work environment with autonomy and opportunities for growth.

How Jobgether Works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.