Isabella Barbosa can be reached at isabellabarbosa@hilton.com for any inquiries. Her address is 2005 Kalia Road, Honolulu, USA and her phone number is +1 (808) 555-1234.
Data Scientist, 2018 - 2020 at Google
- Developed machine learning models to optimize search algorithms and improve user experience.
- Collaborated with cross-functional teams to analyze large datasets and derive actionable insights.

Junior Data Scientist, 2016 - 2018 at IBM
- Conducted statistical analysis on customer data to identify trends and patterns for targeted marketing campaigns.
- Assisted in the development of predictive analytics models to forecast sales performance.
- Proficient in Python, R, and SQL for data manipulation, analysis, and visualization. Skilled in building machine learning models to derive actionable insights from complex datasets.
- Experienced in statistical analysis, hypothesis testing, and A/B testing to drive data-driven decision-making and optimize business strategies.
- Published a paper on "Predictive Modeling in Healthcare" in the Journal of Health Informatics, 2016
- Co-authored a research article on "Machine Learning Applications in Finance" in the International Journal of Financial Engineering, 2017
- Presented findings on "Natural Language Processing for Sentiment Analysis" at the IEEE Conference on Data Science, 2018
- Contributed to a study on "Deep Learning Techniques for Image Recognition" published in the Proceedings of the Neural Networks Symposium, 2019
- Led a team project on "Time Series Forecasting with Recurrent Neural Networks" featured in the Machine Learning Showcase, 2020
Isabella Barbosa can be reached at isabellabarbosa@hilton.com for any inquiries. Her address is 2005 Kalia Road, Honolulu, USA and her phone number is +1 (808) 555-1234.
- Published a paper on "Predictive Modeling in Healthcare" in the Journal of Health Informatics, 2016
- Co-authored a research article on "Machine Learning Applications in Finance" in the International Journal of Financial Engineering, 2017
- Presented findings on "Natural Language Processing for Sentiment Analysis" at the IEEE Conference on Data Science, 2018
- Contributed to a study on "Deep Learning Techniques for Image Recognition" published in the Proceedings of the Neural Networks Symposium, 2019
- Led a team project on "Time Series Forecasting with Recurrent Neural Networks" featured in the Machine Learning Showcase, 2020
- Proficient in Python, R, and SQL for data manipulation, analysis, and visualization. Skilled in building machine learning models to derive actionable insights from complex datasets.
- Experienced in statistical analysis, hypothesis testing, and A/B testing to drive data-driven decision-making and optimize business strategies.
Data Scientist, 2018 - 2020 at Google
- Developed machine learning models to optimize search algorithms and improve user experience.
- Collaborated with cross-functional teams to analyze large datasets and derive actionable insights.

Junior Data Scientist, 2016 - 2018 at IBM
- Conducted statistical analysis on customer data to identify trends and patterns for targeted marketing campaigns.
- Assisted in the development of predictive analytics models to forecast sales performance.
