SENIOR DATA SCIENTIST (30169/30170)

About the Position

We are seeking a talented and motivated Data Scientist to join our team. The professional will be responsible for collecting, processing, and analyzing large volumes of data, transforming them into actionable insights that will support the company's strategic decision-making. We are looking for someone with strong analytical skills, advanced technical abilities, and excellent communication to translate complex findings into clear and useful information for different areas of the business.

Responsibilities

1. Data Collection and Processing: Extract, clean, and organize data from various sources, ensuring its integrity and quality.
2. Exploratory Data Analysis: Conduct in-depth analyses to identify trends, patterns, and improvement opportunities.
3. Predictive Model Development: Create and implement statistical and machine learning models to predict future behaviors and outcomes.
4. Data Visualization: Develop interactive dashboards and reports that facilitate the understanding of generated insights.
5. Interdisciplinary Collaboration: Work together with IT, marketing, operations, and other teams to understand needs and provide data-driven solutions.
6. Continuous Learning: Stay updated on best practices and new tools in data science, proposing innovations that can be applied within the company.

Requirements

1. Academic Background: Bachelor's degree in Data Science, Statistics, Mathematics, Engineering, or related fields.
2. Professional Experience: Minimum of 3 years of proven experience as a Data Scientist or in similar roles.
3. Python Programming: Proficiency in libraries such as pandas, NumPy, scikit-learn, TensorFlow, PyTorch, Keras, among others.
4. SQL Programming: Proficiency in querying and manipulating data in relational and non-relational databases.
5. Data Visualization: Experience with tools like Matplotlib, Seaborn, Plotly, Power BI.
6. Cloud Platforms: Practical knowledge in services from AWS, Google Cloud, or Microsoft Azure.
7. Machine Learning & Deep Learning: Proven experience with supervised and unsupervised learning techniques.
8. Natural Language Processing (NLP): Knowledge in techniques and tools for natural language analysis.
9. Statistics: Solid understanding of statistical concepts and techniques.
10. Code Versioning: Knowledge in Git for version control.
11. MLOps: Experience with automating machine learning pipelines, model monitoring, versioning, and deployment.
12. Docker: Practical experience in containerizing machine learning models and applying best deployment practices in scalable environments.
13. LangChain and LangGraph: Experience with LangChain to integrate language models with external sources (APIs, databases, documents) and with LangGraph to create intelligent and autonomous agents, building interactive and automated workflows.

Differentials

1. Agile Methodologies: Experience with frameworks like Scrum or Kanban.
2. Communication: Ability to translate technical insights into accessible information for non-technical audiences.
3. Problem-Solving: Capacity to approach complex challenges analytically and creatively.
4. Teamwork: Ease in collaborating in multidisciplinary and multicultural environments.