The Manager, Data Scientist role requires expertise in machine learning and deep learning with strong skills in Python, Spark MLLib, Tensorflow, Keras, Pytorch, and Big Data technologies. Responsibilities include managing and guiding data science teams, designing and implementing machine learning and deep learning models, developing ML pipelines on MS Azure cloud, and owning end-to-end marketing ML models such as Churn, CLV, Propensity, and Affinity. Candidates must have 10-12 years of experience with 5+ years in leadership roles, strong programming skills, experience with code repositories and CI tools, and excellent communication skills. The role involves working with distributed teams and directly with clients to deliver data science solutions as part of a marketing intelligence cloud platform. Location is Bengaluru, Karnataka, India.
What you'll do
Provide guidance to the team of Data Scientists and manage machine learning and deep learning projects
Work with the team to analyze complex data structures, manipulate, cleanse data and perform statistical analysis
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Requirements
Strong passion for understanding key business problems
Proven capability to deliver end-to-end analyses by asking the right questions, extracting data, and building predictive models
Expertise in predictive analytics, statistical modeling, data mining, machine learning algorithms and techniques (classification, clustering, regression, multivariate testing)
Excellent communication and interpersonal skills
MS in Computer Science, Math, Physics, or equivalent education/professional experience
10-12 years of total experience with 5+ years managing and leading data science teams
Strong experience in at least one programming language (Python, R, C, C++ is a plus)
Experience working with code repositories and continuous integration tools (Git, Jenkins)
Tech stack
PythonSpark MLLibTensorflowKerasPytorchBig DataHDFSDataBricksMS AzureAzure Data FactoryGitJenkins
Benefits
Employee well-being focusCollaborative work environmentOpportunities for growth through learning, development and career advancementInnovation-driven cultureWork-life balance and flexibilityDiversity and inclusion commitment
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