Senior Data Scientist

Jobtailor; Curitiba; Presencial; BRL 90 000 - 150 000; Tempo integral

Resumo da oferta

Jobtailor seeks a Data Scientist to develop analytical, statistical, and Machine Learning models that drive business decisions and create value. You will collaborate across teams to translate complex problems into data-driven solutions and monitor model performance in production.

Proficiency in Python, SQL, PySpark, and experience with Pandas, NumPy, and Scikit-Learn are essential. Familiarity with MLflow, Databricks, and cloud data architectures is a plus.

Qualificações

• Bachelor’s degree in a quantitative field.
• Solid experience in Data Science and analytical model development.
• Advanced knowledge of Python, SQL, and PySpark.
• Experience handling, transforming, and analyzing large volumes of data.
• Knowledge of Pandas, NumPy, NumPy Financial, and Scikit-Learn.
• Experience with collaborative development and version control using Git.
• Ability to communicate effectively with technical and business teams.
• Preferred: Pyomo for mathematical modeling and optimization.
• Preferred: MLflow for managing the Machine Learning model lifecycle.
• Preferred: Databricks and the Spark ecosystem.
• Preferred: deploying and monitoring models in production.
• Preferred: knowledge of cloud environments and modern data architectures.
• Preferred: optimization, operations research, or advanced mathematical modeling projects.

Responsabilidades

• Develop analytical, statistical, and Machine Learning models to support business decisions and generate value for the organization.
• Explore, transform, and analyze large volumes of data using PySpark, SQL, and Python.
• Build and optimize data processing pipelines in distributed environments, with a focus on performance and scalability.
• Develop, validate, deploy, and monitor Machine Learning models in production environments.
• Partner with business teams to translate complex problems into data-driven solutions.
• Participate in defining metrics, key performance indicators, and analytical studies to track results.
• Apply best practices in software development, documentation, and code version control using Git.
• Contribute to the team’s technical growth and promote best practices in Data Science.
• Deliver models and analytical solutions with measurable business impact.
• Ensure the quality, reliability, and scalability of solutions.
• Automate analytical processes, reducing operational effort and accelerating insight generation.
• Promote the effective use of data through robust, well-documented analyses.
• Ensure the ongoing monitoring, maintenance, and continuous improvement of production models.

Conhecimentos

Machine Learning

Data Analysis

Statistical Modeling

Data Transformation

Analytical Model Development

Python

SQL

PySpark

Git

Pandas

Formação académica

Bachelor’s degree in a quantitative field

Ferramentas

MLflow

Databricks

Spark Ecosystem

Cloud Environments

Descrição da oferta de emprego

• Develop analytical, statistical, and Machine Learning models to support business decisions and generate value for the organization.
• Explore, transform, and analyze large volumes of data using PySpark, SQL, and Python.
• Build and optimize data processing pipelines in distributed environments, with a focus on performance and scalability.
• Develop, validate, deploy, and monitor Machine Learning models in production environments.
• Partner with business teams to translate complex problems into data-driven solutions.
• Participate in defining metrics, key performance indicators, and analytical studies to track results.
• Apply best practices in software development, documentation, and code version control using Git.
• Contribute to the team’s technical growth and promote best practices in Data Science.
• Deliver models and analytical solutions with measurable business impact.
• Ensure the quality, reliability, and scalability of solutions.
• Automate analytical processes, reducing operational effort and accelerating insight generation.
• Promote the effective use of data through robust, well-documented analyses.
• Ensure the ongoing monitoring, maintenance, and continuous improvement of production models.

Requirements

• Bachelor’s degree in a quantitative field such as Computer Science, Statistics, Mathematics, Engineering, or a related discipline.
• Solid experience in Data Science and analytical model development.
• Advanced knowledge of Python, SQL, and PySpark.
• Experience handling, transforming, and analyzing large volumes of data.
• Knowledge of the main Python ecosystem libraries for analysis and modeling, such as Pandas, NumPy, NumPy Financial, and Scikit-Learn.
• Experience with collaborative development and version control using Git.
• Ability to communicate effectively with technical and business teams.
• Preferred: experience with Pyomo for mathematical modeling and optimization.
• Preferred: knowledge of MLflow for managing the Machine Learning model lifecycle.
• Preferred: experience with Databricks and the Spark ecosystem.
• Preferred: experience deploying and monitoring models in production.
• Preferred: knowledge of cloud environments and modern data architectures.
• Preferred: experience with optimization, operations research, or advanced mathematical modeling projects.

Core Competencies

Demonstrates expertise in developing and deploying Machine Learning models, utilizing advanced programming skills in Python, SQL, and PySpark. Capable of transforming large datasets into actionable insights while ensuring the quality and scalability of analytical solutions.

Highest-signal resume keywords

• Machine Learning Model Development
• Data Processing Pipelines
• Python Programming
• SQL Proficiency
• PySpark Expertise

ATS Optimization Keywords

Hard Skills

• Machine Learning
• Data Analysis
• Statistical Modeling
• Data Transformation
• Analytical Model Development
• Python
• SQL
• PySpark
• Git
• Pandas

Soft Skills

• Effective Communication
• Collaboration

Industry Keywords

• Data Science
• Quantitative Analysis
• Performance Metrics
• Optimization
• Operations Research

Tools & Technologies

• MLflow
• Databricks
• Spark Ecosystem
• Cloud Environments