Data Science Research-Statistics Intern
At HPE, we bring together the brightest minds to create breakthrough technology solutions and advance the way people live and work. Our legacy inspires us as we forge ahead dedicated to helping our customers make their mark on the world.
Learning does not only happen through training. Relationships are among the most powerful ways for people to learn and grow, and this is part of our HPE culture. In addition to working alongside talented colleagues, you will have many opportunities to learn through coaching and stretch assignment opportunities. You’ll be guided by feedback and support to accelerate your learning and maximize your knowledge. We also have a “reverse mentoring” program which allows us to share our knowledge and strengths across our multi-generation workforce.
Hewlett Packard Enterprise advances the way people live and work. What sets us apart? Our people. Our people’s relentless commitment to partner, innovate, and act. Hewlett Packard Enterprise helps customers make their mark on the world with cutting-edge technology solutions. We enable our customers to transform industries, markets, and lives by optimizing their IT to be uniquely suited to their needs. We do this by making Hybrid IT simple, powering the Intelligent Edge, and providing the Expertise to make it happen. Our customers' challenges inspire us to advance technology and create solutions - their success is our success.
The Global Marketing organization at Hewlett Packard Enterprise is charted to provide deep insights about customers and channel partners to accelerate demand generation and enhance customer experience. We, in the Marketing Data Science team, are one of the key contributors in this mission by using advanced analytics over massive amounts of digital and transaction data. Over the past few years, we have established a solid track record that has made an impactful influence on HPE strategy and business results. We have also maintained strong research partnership in select areas with leading researchers in academia, and provide a strong encouragement to contribute to the technical community. In 2009, our team was responsible for development and deployment of product portfolio management technology, which resulted in HP winning the INFORMS Edelman Award. In 2012, our team won the INFORMS Revenue Management and Pricing Section Practice Award for outstanding applications of revenue management and pricing methodologies. More recently, our team has been recognized in 2017 and 2019 by ITSMA for its innovative contributions with high business impact in digital marketing and personalization, resulting in HPE winning the first prize on both these occasions.
We are seeking a Research Intern this summer who is interested in developing next-generation solutions that can further accelerate demand generation and enhance the advertising dollars impact. We are looking for individuals very proficient in advanced statistical methods, including but not limited to: MCMC, time series analysis, high-dimensional inference, and semiparametric regression. The minimum duration for this internship is 12 weeks and can be extended to 16 weeks, based on availability of candidate. Applicants are expected to be pursuing a Ph.D. in one of the areas listed above; outstanding Master students may also be considered.
The Research Intern will:
· Work on projects in applied areas including customer segmentation, attribution modeling, marketing campaign design, ecommerce product recommendations, web personalization, conversational engines, and predictive analytics modeling to capture customer requirements.
· Be responsible for building state-of-the-art advanced analytics models while utilizing massive structured and unstructured information.
· Importantly, the candidate should be able to analyze vast amounts of digital and non-digital data to draw business conclusions and provide recommendations to stakeholders.
· We’re looking for people with razor-sharp analytical skills and with the ability to lead projects independently from start to finish.
Required Education, Skills/Knowledge Qualifications
· Pursuing PhD in Statistics, Marketing Science, Computer Science, Operations Research, or Econometrics.
· 0-4 years of relevant experience
· Strong academic background in advanced statistical methods, including but not limited to: MCMC, time series analysis, high-dimensional inference, and semiparametric regression. Strong academic background and skills in empirical work should be demonstrated.
· Graduate level course work in data mining and machine learning.
· Experience, projects, and/or coursework with databases (such as Vertica, MySQL) and programming languages (such as R, Python, Java).
· Experience, projects, and/or coursework manipulating and analyzing complex, large scale, high dimensionality data from varying sources.
· Excellent background in design and development of complex algorithmic software prototypes.
· Work effectively in a cross-disciplinary research team with a focus on developing novel statistical methods motivated by large-scale real world business problems.
· Communicate effectively with statisticians & non-statisticians.
· Applied experience with machine learning on large datasets.
· Excellent programming skills in one or more: R, Python, Java, C, C++, C#, VBA, SQL
· Experiences in handling and preparing large real-world datasets
· Graduate level course sequence in Econometrics and/or Marketing Science.
· Graduate level course work in mathematical optimization.
· Good publication track record at top-tier conferences in Statistics, CS, OR.
• 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!
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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.