DATA SCIENCE COORDINATOR

Inter CarreirasBelo Horizonte, MGJob.bopublished 09/01/2026
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Must-have:PythonAWSAzureGoogle CloudAIFinTech
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Get to Know Inter As pioneers, we transformed the market by launching Brazil’s first digital bank and continue to shape the future with cutting-edge technology. We have evolved into a Global Financial Super App, delivering complete solutions and leading innovation. Here, work has purpose: creating real opportunities, transforming people’s lives, and reshaping the financial market. This is the Inter way of making things happen. If you want to be part of this transformation and leave your mark, your place is here.

Become Sangue Laranja.

About the job and mission of the role

You will be part of our Decision Modelling team and, as a coordinator, you will play a central role in leading the team, ensuring the execution and maintenance of the modeling project portfolio. You will be responsible for delivering solutions, evolving process maturity, governing assets, and developing talent, prioritizing the quality, consistency, and relevance of the delivered models and acting as a bridge between business areas and the technical team.

In your day-to-day, you will:

Lead a team of data scientists in the conception, development, validation, and monitoring of predictive and optimizer models for credit and collections (granting scores, recovery scores, limit optimization, channel propensity, offer personalization, estimated income and revenue estimators).

Interact with business, technology, and risk management areas to understand demands, prioritize projects, and present results clearly and objectively.

Define and evolve the team's technical standard: experimental design, feature engineering, algorithm selection and comparison, calibration, explainability, documentation, and versioning.

Ensure continuous monitoring of models in production, identifying performance degradation and proposing corrective actions.

Ensure the lifecycle of models in production: detection of degradation and drift, retraining, governance, and contribution to MLOps pipelines.

Incorporate, with criteria, the state of the art in the field — contemporary statistical methods, Deep Learning, Generative AI, and Foundation Models — when this generates a measurable gain in predictive power, stability, or operational efficiency.

Contribute to the evolution of the feature store, exploring large volumes of data and unstructured sources, including vector representations (embeddings).

Prioritize the portfolio alongside business, technology, and risk management, and present technical evidence to directorate leadership.

Stay updated with the main trends in the area, actively contributing new techniques, methodologies, and tools that drive the evolution of data science in the team.

Develop the team: mentoring and promoting the Inter culture (Customer Focus, Winning Mindset, Driven by Innovation, Operational Excellence, and One Team).

What we are looking for

Mandatory:

Higher education in Statistics, Mathematics, Computer Science, Engineering, or related fields.

Experience in coordination or leadership of data science teams, with the ability to provide technical guidance and people development.

Solid trajectory in predictive modeling: supervised and unsupervised machine learning and statistical modeling (logistic regression, trees, gradient boosting, and equivalents).

Mastery of Python and the data science ecosystem (scikit-learn, pandas, numpy, and similar).

Mastery of SQL and experience with large volumes of data.

Ability to communicate technical results to non-technical audiences, with clarity and precision.

Intermediate or advanced English for technical literature and interaction with suppliers.

Desirable:

Experience in the financial sector, especially in digital banks or fintechs.

Experience in credit risk models (granting or collections) and/or Internal Ratings-Based for PDD calculation (IFRS 9 / CMN 4966).

Experience with unstructured data, embeddings, LLMs, or Foundation Models applied to business problems.

Knowledge of MLOps practices (MLflow, Airflow, or similar).

Familiarity with cloud environments (AWS, GCP, or Azure).

Post-graduation, certifications, master's, or doctorate in the mentioned areas.

Benefits

Meal voucher and Food voucher

Health and dental insurance

Life insurance

Birthday DayOff

Baby On Board and Breastfeeding Room

Extended parental leave

Daycare/nanny assistance and/or assistance for children with disabilities

Wellhub and Inter Running Group

CARE4U Space (Belo Horizonte): self-care services with exclusive values, decompression area, games, and mini golf course

Advantages in our SuperApp, such as Duo Gourmet

Inter Prime Card

Internal training and capacity building programs

Selection Process Stages

Resume screening;

Screening (initial approach to align expectations about the job);

Assessments;

Interview with the Talent Team;

Technical test (when necessary);

Interview with leadership;

Hiring Proposal (Offer);