Data Scientist – Senior
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Job Description
Data Scientist leading complex analytical projects from conception to delivery for an outsourcing partner. Designing advanced machine learning models ensuring scalability and governance.
Responsibilities:
- Lead high-complexity analytical projects from conception through delivery of solutions that generate measurable value for the organization.
- Design and implement advanced machine learning models, considering aspects such as scalability, governance, fairness and explainability.
- Mentor junior data scientists, providing technical training, deliverable review and career development support.
- Define technical standards and best practices, contributing to the maturity of the area and the consolidation of a strong analytical culture.
- Engage with strategic stakeholders, translating business needs into data-driven solutions and influencing high-impact decisions.
- Ensure the quality, performance and sustainability of models by monitoring results and proposing continuous improvements.
- Participate in strategic and technical forums, representing the data science function and contributing to alignment with the organizational vision.
- Document and share learnings, strengthening the knowledge repository and promoting the team’s continuous development.
Requirements:
- Experience with Python to build and deploy complex machine learning models at scale.
- Strong knowledge of machine learning model lifecycle management and deployment best practices.
- Extensive experience with Databricks for building end-to-end machine learning pipelines.
- Advanced knowledge of data virtualization tools such as Denodo for efficient data access.
- Proficiency in Teradata to optimize data storage and query performance.
- Leadership skills to drive MLOps initiatives and foster innovation in machine learning operations.
- Strategic thinking to align MLOps processes with business objectives and goals.
- Excellent communication skills and stakeholder management across all organizational levels.
- Ability to navigate complex technical challenges and deliver effective solutions.
- Resilience and adaptability to thrive in a dynamic, fast-paced environment.
- Classification models (trees, ensembles, logistic regression, etc.).
- Evaluation metrics (AUC, Precision, Recall, F1, KS, etc.).
- Techniques for handling extreme class imbalance (SMOTE, undersampling, oversampling).
- Data cleaning, transformation and feature engineering.
- Knowledge of transactional and financial data.
- Advanced use of MLflow for experiment tracking and logging.
- Unity Catalog for model registration.
- Model versioning (metrics validation, model promotion).
- Model Serving for production deployment.
- Hyperparameter optimization (Grid Search, Random Search, Hyperopt, Optuna).
- Threshold calibration (e.g., CalibratedClassifierCV).
- Spark for large-scale processing (Spark SQL, PySpark).
- Automation of training and deployment pipelines.
- GitLab for project versioning.
- Databricks as the development platform.
Benefits:
- Meal and transportation allowance;
- Health insurance;
- Semi-annual performance reviews with growth opportunities;
- Feedback-driven culture;
- Maternity/paternity leave;
- Referral bonus;
- ZenKlub – two free sessions per month;
- Education benefit;
- Length-of-service award;
- Office space available for coworking in Porto Alegre (RS).
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