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Offers “Hp”

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Expert Business Intelligence Finance Data Scientist

  • Internship
  • Guadalajara, MEXICO

Job description

Expert Business Intelligence Finance Data Scientist



Job Description:



Hewlett Packard Enterprise advances the way people live and work. We bring together the brightest minds to create breakthrough technology solutions, helping our customers make their mark on the world.

The Finance team at HPE provides world class decision support driving profitable growth and exceptional shareholder value through our commitment to operational excellence, people development, and innovation. We provide accurate and timely financial information meeting the company’s regulatory and fiduciary responsibilities with unwavering integrity. Our objective is to display all business activities in a financially correct and transparent manner.
In the Finance division, our reporting and controlling teams are working on tasks like asset management, financial integration of mergers and acquisitions, as well as USGAAP-reporting. Our objective is to display all business activities in a financially correct and transparent manner.

Finance is looking for a motivated and energetic individual to join the exciting Finance Business Intelligence team for a Data Scientist role to provide inputs, ideas and improvements to our finance organization.

Finance Business Intelligence Analysts provide financial leadership and support to the processes and teams in Finance. We are responsible for reporting, analyzing and providing business insight and analytics for QTD Secured, historical balances, pipeline, orders and sales out information for Finance, Sales, Sales Operations and Supply Chain teams, including individual contributors and Senior VPs.

The ideal candidate is adept at using large data sets to find opportunities for product and process optimization and using models to test the effectiveness of different courses of action. They must have strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms and creating/running simulations. They must have a proven ability to drive business results with their data-based insights. They must be comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.

Responsibilities may include:

· Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
· Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies.
· Assess the effectiveness and accuracy of new data sources and data gathering techniques.
· Develop custom data models and algorithms to apply to data sets.
· Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes.
· Develop company testing framework and test model quality.
· Coordinate with different functional teams to implement models and monitor outcomes.
· Develop processes and tools to monitor and analyze model performance and data accuracy.
· Qualifications for Data Scientist.

Education and Experience Required:

5-7 years of experience manipulating data sets and building statistical models, has a Master’s or PHD in Statistics, Mathematics, Computer Science or another quantitative field, and is familiar with the following software/tools:

· Coding knowledge and experience with several languages: C, C++, Java, JavaScript, etc.
· Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
· Experience querying databases and using statistical computer languages: R, Python, SLQ, etc.
· Experience using web services: Redshift, S3, Spark, DigitalOcean, etc.
· Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
· Experience analyzing data from 3rd party providers: Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, Facebook Insights, etc.
· Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.
· Experience visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3, ggplot, etc

Knowledge and Skills Required:

· Strong analytical skills and financial/IT modeling capabilities.
· Strong problem-solving skills
· Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets.
· Experience working with and creating data architectures.
· Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
· Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
· Excellent written and verbal communication skills for coordinating across teams.
· A drive to learn and master new technologies and techniques.
· Excellent project management skills.

We offer:

· A competitive salary and extensive social benefits
· Diverse and dynamic work environment
· Work-life balance and support for career development

An amazing life inside the element! Want to know more about it?
Then let’s stay connected!

https://www.facebook.com/HPECareers
https://twitter.com/HPE_Careers

Job:
Finance

Job Level:
Expert



Hewlett Packard Enterprise is EEO F/M/Protected Veteran/ Individual with Disabilities.



HPE will comply with all applicable laws related to the use of arrest and conviction records, including the San Francisco Fair Chance Ordinance and similar laws and will consider for employment qualified applicants with criminal histories.