Are you fascinated bymachine learning and data science? Would you like to join anemerging team focused on operational analytics in a growth company with anamazing culture?
If so, we have anopportunity for you to use your analytical skills to convert data intopredictive insight and meaningful information that enables business processimprovement.
Come be part of a Data Enrichment team that leads the creation andimplementation of new models / processes that bring measurable qualityimprovement to the Experian BIS Commercial Data Repository.
What you will do :
Create and implement machine learning models to improve dataquality
Own and iterate on previously created models
Developing tools for data processing and information retrieval
Documentation of model performance and features
Collaborate with technology and other business teams
Continual learning of new technologies and data science
Data visualization ofperformance metrics
1 year of working experience in data model development and implementation
Ability to independently support existing products
Experience with supervised machine learning methods and concepts
Proven ability to work on models with large, complex datasets
Intermediate to proficient experience in Python and / or R is required
Robust knowledge and experience with statistical methods
Source code management skills using tools like Git
Preferred degree in Machine Learning, Computer Science, ElectricalEngineering, Physics, Statistics, Applied Math, Information Technology or otherquantitative fields (Or in pursuit of the same)
Proven previous job stability, including maintaining long-term work relationships with former employers
Must be able to clear the company’s pre-employment screening
Knowledge of SQL
Experience with Hadoop and NoSQL related technologies such as MapReduce, Spark, Hive, Pig, HBase, mongoDB, Cassandra, etc.
Knowledge of NLP / Text mining techniques and related open sourcetools
Knowledge of Bayesian statistical inference and related machinelearning methods
Añadir a los favoritos
Eliminar de mis favoritos
Debes iniciar sesión en tu cuenta para agregar este empleo a tus favoritos. Haz clic en "Continuar" para acceder a tu cuenta o crear una cuenta nueva. Luego de iniciar sesión, podrás ver y organizar tus favoritos tanto en nuestro sitio web como en la aplicación móvil.