Data Scientist Junior

About Fligoo

Fligoo is an emerging leader in the Enterprise AI category, having built a technology architecture and suite of products that enable rapid development, deployment, and scaling across large enterprises.

At the moment, we are developing the next generation of AI technology for the Wealth Management and Financial Services industries. Fligoo’s technology leverages AI to allow the most important firms in the world to increase wallet share, predict client attrition, retain financial advisors, increase ROE, among others, significantly growing and protecting AUM. Companies we partner with include Wells Fargo, Cetera, Broadridge, PNC, JPMorgan, among others.

With the acquisition of Mobi To Buy and with offices around the globe in San Francisco, New York, São Paulo, Rio de Janeiro and Córdoba, Fligoo is currently one of the biggest Artificial Intelligence companies in the world.

Our long term vision is to become the most recognized technology company through our work helping our clients improve people’s lives offering solutions to complex problems by leveraging the most valuable asset of our time: data.

About Position

**Position**: Full-time

**Responsibilities**:

- Contribute in defining hypotheses and proposing analytic approaches based on available data.
- Collaborate to the development and execution of tests to validate hypotheses.
- Work on understanding data sources and extracting relevant information.
- Collaborate to perform data quality checks to ensure the effectiveness and reliability of data.
- Learn to the definition of feature extraction schemas in collaboration with the team.
- Contribute to communicate findings and results through effective data storytelling resources.
- Assist in validating running solutions through hypothesis testing.

**Qualifications**:
Currently pursuing a degree in Computer Science, Statistics, Mathematics or other quantitative fields - **Desire**

Technical Skills:

- Basic understanding of Python libraries: Pandas, Numpy, Matplotlib, Seaborn, GGPlot.
- Knowledge of machine learning libraries: Scikit-learn, XGBoost, Keras.
- Exposure to data visualization tools and frameworks.
- Familiarity with Linux/MacOS/Windows operating systems.
- Basic proficiency in a Python IDE (e.g., Pycharm or VIM).
- Desirable: Exposure to project task management platforms (e.g., JIRA, Trello).
- Desirable: Familiarity with code versioning using systems like Git.
- Desirable: Understanding of testing methodologies (e.g., pytest).
- Desirable: Awareness of virtualization concepts, such as Docker.

Other Skills
- Demonstrating a strong interest and aptitude for learning new technologies and methodologies.
- Intermediate English proficiency, enabling participation in discussions on technical topics.

**Experience** (Desirable)**:

- No specific prior professional experience is required.
- Familiarity or exposure to descriptive, diagnostic and prescriptive analytics.
- Understanding of correlation analysis techniques (e.g., scatter plots, correlation matrices).
- Basic familiarity with time series plots and variable importance.
- Exposure to tabular data, time series, and tree-based algorithms (e.g., Random Forest, Gradient Boosting Machines).
- Exposure to communication tasks related to presenting analytic results.
- Understanding of supervised and unsupervised learning concepts.
- Exposure to A/B testing campaigns is a plus.