Marketing data scientist
description -
working within the advanced analytics group at hp, you will apply your advanced data science skills to solve a variety of problems in marketing analytics. The successful candidate will have strong technical skills including r and python, with at least 3 years of experience. We are looking for someone with experience in multi-touch attribution and marketing mix modeling, from model development to optimization. The ideal candidate will help with our project architecture, including the development of applications using frameworks such as shiny, flask or react. The new colleague will contribute to the development of innovative solutions, lead complex projects and be comfortable presenting to senior leadership.
role responsibilities:
1. works with project team to understand problem statements, initiatives, and direction.
2. builds statistical models to evaluate the impact of marketing activities for hp.
3. stays up to date with technology and trend shifts.
4. collaborates and communicates with project team regarding project progress and proactively identifies solutions to issues.
5. collaborates with project team and lead data scientists.
6. typically partners with high-level individual contributors and managers.
7. supports projects requiring data engineering solutions expertise.
role requirements:
1. bachelor's or master's degree in mathematics, economics, physics, biology, computer science, or equivalent.
2. typically 3-5 years of experience – it may include also graduate or postgraduate research.
3. experience with r and python for data manipulation, visualization and statistical model development.
4. familiarity with data science web application frameworks such as shiny, streamlit and/or react.
5. effective utilization of distributed cloud-computing data science platforms.
6. ability to create compelling stories with data to describe and communicate data insights.
7. excellent written and verbal communication skills; mastery in english and local language.
what distinguishes us:
1. huge databases.
2. best in class methodologies.
3. 1 day per month dedicated to trainings by choice.
4. super collaborative environment and colleagues in many different countries.
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