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Big Data

As a member of our Big Data team, you’ll work with awesome people in a start-up environment. With one of the biggest data sets in the world, you’ll use research-driven analytics to tackle our business challenges and drive the innovation that changes the lives of our customers. That means your work can impact major decisions that go all the way to the top. This is your chance to turn the massive scale of possibilities at AT&T into an even bigger opportunity.

Senior-Inventive Scientist (Labs- Big Data Research)

New York , New York

External and Internal
In AT&T Labs' Big Data Research organization, we develop new methods for analyzing massive data sets to learn about our network and our customers, and to improve services and operations. We are a pioneer in big data technologies – developing  novel methodologies for statistical computing, event prediction, fraud detection, visualization, text mining, data stream management, data quality and anomaly detection, and privacy preserving measures such as data anonymization.
We are looking for strong analysts in a data science field with a passion for extracting insights from large data sets.  We work on problems that cut across all aspects of AT&T's business, including network and service management, marketing and customer care, network design and optimization, and new service creation.  Many analysts work directly with business unit leaders.   All have opportunities to invent novel, publishable methodology that address real-world problems.   Increasingly we contribute to multiple data science disciplines through open source projects, led by AT&T. 
We typically hire to fill two positions:
  1. Our PhD level Inventive Scientists are technical leaders in their field, establishing external recognition while creating innovative, business-impacting solutions. 
  2. Data Scientists will usually have a Master’s; they are responsible for the entire data – to – solution pipeline.  They both support the Inventive Scientists and are responsible for their own projects.
  3. Location can be Bedminster, NJ or New York, NY.
    Desired areas of expertise:
    - Spatiotemporal analysis of mobile data - The AT&T network generates billions of records per day from handheld devices, each one providing a data point in time and space.  Understanding the dynamics of this data through time series and spatial statistics helps AT&T better serve its customers, improve its network, and develop new products and services.
    - Statistical computation - AT&T is developing high-performance statistical models and algorithms (mostly in R) to leverage modern streaming and storage technologies. One example is the AT&T collaborative coding project RCloud  http://rcloud.social/.
    - Text mining - AT&T is modeling free-form text from millions of customer surveys,  representative notes, and chats as one way to manage our business in real time. New methods are needed to keep up with the volume and complexity of this data.
    - Data visualization - AT&T creates algorithms, systems, and techniques for visualizing new data sets at increasingly large scale. AT&T is especially interested in interactive visualization of very large geospatial and temporal data. One example is our open source project Nanocubes  http://nanocubes.net .
    - Database management and data quality - AT&T is building high-performance and low latency systems to manage high velocity data streams. These systems are critical for accurate processing and analysis of data from the AT&T mobile network. Analyzing the data to identify glitches, and cleaning the data to minimize distortion while preserving maximal underlying variation, are essential to ensure meaningful big data analytics.
    - Predictive Modeling – We estimate models to predict customer behavior and sentiment, service disruptions, and event impacts.  Models serve to both target, and to gain insight into variable relationships.  Our rich customer and network data allows analysts to positively affect business outcomes and to pursue methodological research (comparing machine learning algorithms, for example). 
    Please visit http://www.research.att.com to learn more about AT&T Research. 

    Qualifications
    - Inventive Scientist candidates shall have a PhD or equivalent in a data science field, including these most commonly:  Statistics, Computer Science, Machine Learning, Operations Research, Computer Engineering, and Mathematics.
    - Data Science candidates shall have a Master’s or PhD in a data science field.
    - Experience and passion for finding solutions to real world, applied problems. In addition for Inventive Scientists, established excellence in applied research through publications, new research techniques, open source contributions, and patents.
    - Expertise working with large industrial scale data sets, and the ability to use software to manipulate data, prototype new tools, and extract actionable insights from that data.
    - Capable of presenting outcomes of analytic solutions in a format easily understood by a non-technical audience.
    - Expert-level skills and abilities within field of knowledge, and demonstrated ability to develop new ideas from inception to prototype.
    - Accomplished R programmer, and experience coding with other languages such as C/C++, Python, and Java. 
    - Experience with modern data management systems like Hadoop, NoSQL, and Spark.
    - Candidates with 5+ years of relevant experience may be considered for a more senior position.
    Qualified candidates may be asked to submit a Research Statement (required for Inventive Scientist applicants) and 3-5 references (required for Inventive Scientist applicants, optional but encouraged for Data Scientist applicants). 

Requisition ID 1752282-1 If you’re not ready to apply,
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"A company committed to transforming itself for the future."

Five star employee rating

Former Employee - Data Scientist Intern in Plano, TX

Pros

In a technical community, you do have lots of freedom to determine what to work on. The company is truly embracing changes and transformation to the future, open to new ideas
Great leadership (bootcamp style) and technical training programs

Cons

Great vision from senior officers, execution strategy some times legging at mid-level managers


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