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Company Info
PLANO, TX, United States

Company Profile

Data Scientist


Job ID:



Bangaluru, IN-KA, India 


Big Data Analytics, Big Data Architect, C++ #, Consumer Analytics, Data Mining, Data Scientist, Hadoop

Job Views:


Employment Type:

Full time



Job Description:


  • Minimum 2 years, post academia, professional experience delivering models and algorithms that drive business decisions.
  • 7-10+ years’ experience in Marketing Analytics (including statistical data mining, data analytics, and working with structured and unstructured datasets)
  • Experience with segmentation/cohorting strategies and techniques a must
  • Knowledge of database design, applications, and data flows preferred
  • Hands-on experience with SQL, Hadoop, R, SPSS, or comparable analytic tools a plus
  • An advanced degree in Marketing Analytics, Statistics, Computer Science or related field a plus.  Good working knowledge of various statistical methods and demonstrated capability to apply it to broad range of customer data including both structured and unstructured data
  • The ability to thrive in fast-paced environments, and is flexible and able to handle rapidly-changing scenarios; sees ‘ambiguity’ as an opportunity rather than a hurdle
  • An unyielding team-first mentality, with a willingness to “lean in” in a start-up environment
  • Strong knowledge of data mining algorithms including decision trees, probability networks, association rules, clustering, regression and neural networks
  • Strong Algorithmic and programming skills. Ability to created and program new statistical algorithms
  • Exposure to technologies in Big Data spac (Hadoop Stack like M/R, HDFS, Pig, Hive, HBase, Flume, Sqoop, etc..NoSQL stores like Cassandra, HBase etc)
  • Outstanding communication skills, including proven experience in making complex subject matter accessible to a broader audience
  • You’re a change agent: see ‘ambiguity’ as an opportunity as opposed to a hurdle, thrive on challenging yourself to push beyond conventional thinking.
  • In depth knowledge of standard algorithms such as linear regression, logistic regression, clustering, decision trees, text analytics and affinity analysis
  • Expert SQL skills including complex query structures
  • Track record of successful implementations of quantitative, data-driven products in a business environment
  • Hands-on experience with optimization, data mining, machine learning or natural language processing
  • Ability to analyse user level data (cookie-level, email-level, direct mail-level, etc.) using SQL for the purpose of uncovering patterns and creating insights and recommendations for clients
  • Experience with any combination of the following: Data Visualization Tools: QuickView, Spotfire, Tableau, yWorks, BI Reporting: Business Objects, Cognos, MicroStrategy
  • Strong presentation skills, that is, excellent story-telling skills

Job Requirements:

  • Conduct deep-dive analyses of customer  sales (lead scoring, risk scoring, opportunity analysis), marketing (email, acquisition analytics etc.,), product (log analysis, click stream data) , customer care (predictive call volume models) patterns and offer redemption history to uncover insights into key performance drivers
  • Develop data-derived consumer cohorts/segments and statistical models for marketing, offer optimization and offer assignment to improve consumer targeting and ad yield management
  • Promote the continued adoption of ‘Test & Learn’ approach to maximize return on marketing actions and working with stakeholders in translating analytical findings into actionable insights
  • Passionate for continuous learning, experimenting, applying and contributing towards cutting edge open source technologies and software paradigms
  • Measurably impacting company performance by delivering high quality scalable products
  • Discovering actionable insights from data and presenting them to others through rich visualizations
  • Responsibility for design, implementation and support of key data sciences products and production systems
  • Knowing, using and evangelizing best practices in working with people, software and data
  • Establishing close collaboration with product and engineering teams and interacting with other teams on a regular basis
  • Formulation, design, implementation, testing and validation of machine learning models
  • Assembly of modeling data sets from multi-terabyte structured and unstructured data repositories

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