Data Scientist

KSTACK; São Paulo; On-site; BRL 167,400 - 279,000; Full time

Job summary

A leading financial technology firm in São Paulo is looking for a Data Scientist to develop predictive models that enhance the performance of financial advisors. This role requires deep expertise in XGBoost and Python, as well as experience in building real-time recommendation systems. Candidates should have a strong background in machine learning and the ability to work within the financial advisory sector. The position is an opportunity to drive significant business impact through innovative data solutions.

Qualifications

• Proven track record of delivering production-grade ML use cases focused on personalized recommendations or business decisions.
• Experience in building ML models that drive real business impact, especially in financial advisory.

Responsibilities

• Behavioral Feature Engineering.
• Peer-Group Clustering.
• Explainable AI implementation (SHAP / LIME / others).
• Developing models that forecast actions leading to higher advisor performance.
• Building real-time recommendation systems.
• Delivering production-grade use cases focused on personalized recommendations and business decision support.

Skills

Strong communication skills

Deep experience with XGBoost / LightGBM

Expert-level Python

Expert-level SQL

Experience with Amazon SageMaker

Familiarity with MLOps

Knowledge of Graph Databases

Experience with K-Means / DBSCAN or similar clustering techniques

Tools

Machine Learning libraries

Job description

Objective

The objective of this role is deciphering the “DNA” of RJ’s best-performing financial advisors and developing a real-time recommendation system that pushes each Financial Advisor (FA) in RJ to reach new performance levels.

This role goes beyond reporting historical data. The Data Scientist will develop predictive models capable of forecasting the actions that lead to success and integrate those insights directly into the FA workflow, enabling data-driven decision-making.

Key Feature for Success in This Role

• Strong communication skills

Key Responsibilities (including but not limited to)

• Behavioral Feature Engineering

• Peer-Group Clustering

• Explainable AI implementation (SHAP / LIME / others)

• Developing models that forecast actions leading to higher advisor performance

• Building real-time recommendation systems

• Delivering production-grade use cases focused on personalized recommendations and business decision support

Technical Requirements (including but not limited to)

• Deep experience with XGBoost / LightGBM

• Experience with K-Means / DBSCAN (for clustering) or similar advanced clustering techniques

• Proven track record of delivering production-grade ML use cases focused on personalized recommendations or business decisions

• Knowledge and exposure to Graph Databases, including the ability to query graphs to create features for ML models

• Expert-level Python, SQL, and Machine Learning libraries

• Experience with Amazon SageMaker

• Familiarity with MLOps

Domain Knowledge

• Wealth Management

If you have experience building ML models that drive real business impact, especially within financial advisory or wealth management environments, this could be a great opportunity.