Lead Data Scientist

Exp: 6 - 10 years

Preferred: Talents from Tier 1 Global Schools


  • Advanced degree (Ph.D. preferred) in Engineering, Science, Mathematics, or related
  • Expert knowledge of probability, statistics and machine learning theory including experience in: Deep Learning, Clustering, Decision Trees, Logistic Regression, Dimensionality Reduction, and Random Forests for prediction and recommendations.
  • Expert knowledge of Experimental Design and Statistical Decision Theory
  • 3+ years working with business stakeholders as a trusted adviser in Data Science and Monetization
  • 3+ years providing mentorship, education, and thought leadership to organizational stakeholders regarding best practices in data science
  • Microsoft IoT/data science toolkit: Azure Machine Learning, Datalake, Datalake analytics, Workbench, IoT Hub, Stream Analytics, CosmosDB, Time Series Insights, PowerBI
  • Experience working in a start-up environment, preferably in an IoT/AI company
  • Preferred: Building IoT analytics models, including failure diagnosis and failure prediction
  • Preferred: Executing customer advanced analytics, including marketing mix analysis, segmentation, retention modeling, targeted marketing, basket analysis, next product recommendation

Role:

  • Gather and analyze data, devise innovative data science solutions and build prototypes to enable development of high-performance algorithms in scalable, product-ready code
  • Understand problems from the client’s point of view, build and execute solid analytics work plans, gather and organize large and complex data assets, perform relevant analyses (data exploration and statistical modeling), manage priorities and deadlines, foster teamwork in interactions, develop client relationships with client counterparts, and communicate hypotheses and findings in a structured way
  • Passion for understanding business problems and trying to address them by leveraging data - characterized by high-volume, high dimensionality from multiple sources
  • Design, develop and implement real-time, highly complex advance machine learning models to solve real time company problems
  • Experience with building predictive statistical, behavioral or other models via supervised and unsupervised machine learning, statistical analysis, and other predictive modeling techniques
  • Contribute to Company IP and patent portfolio/s
  • Extensively publish in NIPS, Kaggle, JML, ICLR, nature ,nuerocomputing. etc

 

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