Sr. Data Analytics

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With more than 170,000 people in over 40 countries, Capgemini is one of the world's foremost providers of consulting, technology and outsourcing services. The Group reported 2014 global revenues of EUR 10.5 billion. Together with its clients, Capgemini creates and delivers business and technology solutions that fit their needs and drive the results they want. A deeply multicultural organization, Capgemini has developed its own way of working, the Collaborative Business ExperienceTM, and draws on Rightshore®, its worldwide delivery model.

Learn more about us at http://www.capgemini.com/ .

 

Rightshore ® is a trademark belonging to Capgemini

Capgemini is an Equal Opportunity Employer encouraging diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law.

 

Sr. Data Analytics
 

Job Description : 

10+ years of relevant years of experience

Ability to interface with business team / traders to understand the requirements and conceptualize & explain analytical solutions

Ability to design and develop detailed use cases

Ability to design algorithms

Work/guide the analyst/developers in designing and reviewing the solutions

Strong presentation and communication skills

 

Job Responsibilities:

Develop data analytics on a big data platform

Perform analytics on huge data volumes on various business domains like  (but not limited to) – AML, Trading, Payments, Risk etc.

Understand business requirements and identify analytics techniques, perform data preparation & profiling using statistical methods , design & develop appropriate models (exploratory, computational and  quantitative analytics)

Performance testing of the models

Documentations and reporting of the models and their results

 

Technologies:

Java Map reduce programming

Python or Pig scripting

Advanced Hive QL programming including (but not limited to) table functions, partitioned table function, windowing, npath analysis etc.) that Hive provides along with basic Hive sql.

Knowledge of machine learning algorithms is a strong plus (some examples):

Reinforcement Learning algorithms – e.g. HMMs, MDPs, Kalman filters

Unsupervised learning algorithms towards ‘pattern / relationship hunting’ – e.g. K-means clustering, Hierarchical clustering, PCA

Range of Classification algorithms – e.g. KNN, SVMs, Logistic Regression

Catch all – support for being able to mathematically determine ‘equilibrium-zones’, ‘clusters’ and ‘deviations’ / ‘exceptions’ in data via various machine-learning approaches

 

Location: NYC, NY