* Send resumes to [email protected]
<[email protected]>*




*Position: Sr. Data Analytics Lead*

*Location: New York City*

*Duration: 6 Months possibly longer.*



*Need H1B Copy*



•             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



Regards

Philip

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