Senior Data Scientist

About FligooFligoo is developing the next generation of AI technology for large customer-based enterprises.
Fligoo's technology leverages AI to allow the most important financial services & wealth management firms in the world to understand their customers, increase sales, predict client attrition, retain advisors, and increase ROI, among others, significantly growing and protecting assets.
Companies we partner with include Wells Fargo, Broadridge, Bradesco, Mastercard, Basf, among others.2-min video: About PositionOverviewLocation: Córdoba (not fully remote).
Position: Full-TimeJob PurposeTheData Scientistis responsible for analyzing data sources in a business context to propose and implement an analytic approach to solve a business problem.
The ideal player is a competent data wrangler with broad experience in several domains, who enjoys analyzing and solving problems.
He or she will translate data findings into business insights, so the data scientist must be comfortable interacting with data and getting correct conclusions from them, as well as implementing a proper analytic task to solve the problem.
The Data Scientist will provide support on tasks related to data preparation, help to define and implement the analytic tasks, and guide during the development and deployment of a Data Science solution.
ResponsibilitiesMaintain communication with stakeholders to understand business needs and client pain points.Define the required data to address these pain points and design solutions that add value to the business.
Process data effectively using tools such as S3, Athena, and Jupyter Notebooks.Develop exploratory data analysis (EDA) to validate data with clients and generate insights through data storytelling.Identify the type of problem (classification, regression, time series, etc.) and select the best solution approach to add value quickly.Implement data processing and feature engineering to prepare data before model training and testing.Assess data effectiveness by conducting data quality checks.Design, build, tune, and test algorithms, and deploy models using best practices to ensure reproducibility in test and production environments.Develop strategies to validate model results in production.Qualifications1.
Academic trainingDegree in Computer Science, System Engineering, Statistics, Mathematics or other quantitative fields.
A master's or Ph.D. in those fields is a plus.2.
Technical SkillsExperience with data science toolkits such as Python, Jupyter Notebooks, or Spark.
Knowledge of the object-oriented programming paradigm.Experience with Python data science libraries such as Pandas, Numpy, Scikit-learn, keras, XGBoost and LigthGBM (knowing Keras, Pytorch, or TensorFlow is a plus).
- Experience with data visualizations tools such as Matplotlib, Plotly, Seaborn or GGPlot.
- Experience with AWS cloud infrastructure (i.e. S3, SageMaker, Athena, Lambdas, etc.)- Knowledge of the Scrum framework for project management.
Proficiency in using task management tools like Trello or Jira.
- Experience with version control systems like Github or Bitbucket.
- Familiarity with virtualization technologies like Docker.3.
Other SkillsAdvanced English proficiency.Experience5+ years working on real-world projects, including implementing supervised and unsupervised machine learning models.5+ years performing project tasks and bug tracking on platforms like JIRA or Trello.5+ years conducting analytics on real data sources, including:Descriptive Analytics.
Diagnostic Analytics.
Prescriptive Analytics.
5+ years creating advanced charts on real data sources, including:Distribution descriptors (e.g., box-plot, histogram, pie chart).
Correlation to target variables (e.g., scatter plots, correlation matrix).
Time series plots.
Variable importance.
Business language reporting.
Feature extraction in real-life projects involving data types such as:Tabular data.
Time series.
Text.
Experience with tree-based algorithms (e.g. Random Forest, Gradient Boosting Machines).3+ years performing communication tasks regarding analytics results, including the ability to adapt technical language to non-technical audiences depending on the interlocutor.At least 2 real-life projects having an A/B testing campaign performed (desirable).