Senior Data Scientist

Company:
Location: Bengaluru, Karnataka

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PeopleCloud Customer is a world-class cloud-based Customer Data Platform (CDP) fully enabled with a complete Marketing Automated Operating System (MAOS). The PeopleCloud Customer platform provides out-of-the-box SaaS and PaaS products that are fully integrated, including Customer Identity services, Deterministic and Probabilistic Customer Record stitching services and Marketing Machine Learning algorithms and models trained to deliver personalized activation at scale.

Epsilon India team is looking for a talented team player in a Senior Data Scientist. You are an expert, mentor and advocate. You have strong machine learning and deep learning background and are passionate about transforming data into ml models. You welcome the challenge of data science and are proficient in Python, Spark MLLib, Tensorflow, Keras, ML algortihms and Deep Neural Networks, Big Data. You must be self-driven, take initiative and want to work in a dynamic, busy and innovative group.

You will work with a distributed team (onshore and offshore) and work closely with a broadly talented team of delivery management, business analysts, visual designers, analytics, developers, and QA. You will work directly with clients to own data science solutions as a member of the Data Sciences MML Cloud Services team, and will operate as part of the product team to extend the Platform functionality when not supporting client projects

  • Perform hands-on analysis of large volumes of web analytics, transaction, customer data, second and third-party data. Work with complex data structure, manipulate, cleanse data and perform statistical analysis
  • Design and Implement Machine learning models using Spark ML, Python, Map-Reduce, Hive, HDFS, Spring, Hibernate and Java
  • Design and implement Deep neural network models using Tensorflow, Pytorch, Keras and Python
  • Create engaging and meaningful data visualizations of findings linked to clear client business impact.
  • Develop machine learning pipelines with big data design principles in MS Azure cloud using Azure Data Factory
  • Own end to end implementations of Marketing machine learning models such as Churn, CLV, Propensity, Affinity models.

Qualifications

  • Experience with large scale distributed databases and computing systems like Hadoop, HDInsight or DataBricks
  • Strong passion for understanding key business problems, bringing together a team to understand data/ instrumentation needs and/or mine through data to unearth deep insights into customer experiences
  • Proven capability to deliver end-to-end analyses by asking the right questions, extracting data, and building predictive models to ensure actionable results.
  • Excellent communication & interpersonal skills with an ability to communicate ideas.
  • MS or Ph.D. in Computer Science, Math, Physics, or equivalent education/professional experience is required.
  • 6+ years of overall IT experience with 2+ years in a data science role with demonstrable experience
  • Deep experience in machine learning with Spark and Azure Machine Learning and Cognitive Services.
  • Azure Cloud experience required. Azure Data Factory experience preferred.
  • Strong experience in DNN models using Tensorflow v1.8 above, Keras, Pytorch
  • Experience with sequence modeling using RNNs/LSTMs is good to have
  • Strong experience in at least one database technology (i.e. Hive, PrestoDb etc.)
  • Strong experience in at least one programming language (i.e. Python, R, C, C++ is plus)
  • Experience working with different query languages (i.e. PL-SQL, T-SQL)
  • Understanding and experience working with cloud infrastructure services like Azure and Amazon Web Services. Azure preferred.
  • Experience working with code repositories and continuous integration (i.e. Git, Jenkins, etc.)
  • Strong passion for understanding key business problems, bringing together the team to understand data/ instrumentation needs and/or mine through data to unearth deep insights into customer experiences

*** Mention DataYoshi when applying ***

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