Lead Data Scientist

5 Months ago • 8-12 Years • Artificial Intelligence • Data Analyst

Job Summary

Job Description

Lead Data Scientist responsible for designing and deploying analytical systems end-to-end. Requires 8-12 years of experience in Big Data, Business Intelligence, Data Warehousing, AI, and Machine Learning. Experience with production-grade solutions for end customers is crucial. Strong programming skills (R, Python, or Java) and familiarity with various data science platforms (RapidMiner, SAS, IBM) are essential. Expertise in machine learning algorithms, statistical inference, and BI tools (Tableau, Oracle, etc.) is required. Experience with NoSQL (MongoDB, Cassandra, Hadoop, Spark), RDBMS (Oracle, SQL Server), and cloud computing frameworks (AWS, Azure) is also needed.
Must have:
  • 8-12 years Big Data/AI/ML experience
  • Production-grade solution deployment
  • R/Python/Java programming skills
  • Machine learning algorithm expertise
  • BI & Data Science platform experience
  • Cloud computing (AWS/Azure) knowledge
Good to have:
  • NLP toolkits and deep learning frameworks
  • Experience with specific NoSQL and RDBMS tools

Job Details

About the job

  • A Computer Science Degree (Bachelors or Masters), Mathematics or Statistics (Masters)
  • Total of 8-12 years of Big Data, Business Intelligence, Data warehousing, Artificial Intelligence and Machine Learning experience
  • At least 2 years of experience in data science in rolling out production grade solutions to end customers

Technical Skills

  • Experience in designing and deploying Analytical Systems, End to end.
  • Excellent problem-solving skills
  • Strong verbal and written communication skills
  • Strong hands-on programming experience with R or Python or Java
  • Hands-on experience with any of the data science platforms such as RapidMiner, SAS and IBM
  • Sound understanding of machine learning algorithms, including classification, clustering, association and recommendation generation
  • Familiarity with Open Source Machine Learning (R, Python), NLP toolkits and deep learning frameworks (TensorFlow, Keras, H2O etc.)
  • Working experience with statistical inference
  • Experience with BI (Tableau, Oracle, IBM, Microsoft), Data Science (RapidMiner/ SAS/ IBM), NoSQL (MongoDB, Cassandra, Hadoop, Spark), RDBMS (Oracle, SQL Server) tools
  • Experience using cloud computing and storage frameworks such as Amazon AWS (EC2, S3, Redshift, RDS) and Microsoft Azure Storage

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