Expert, Data Scientist

• Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
• Develop custom data models and algorithms to apply to data sets.
• Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting, and other business outcomes.
• Develop company A/B testing framework and test model quality.
• Coordinate with different functional teams to implement models and monitor outcomes.
• Develop processes and tools to monitor and analyze model performance and data accuracy.

Requirements

• Strong problem-solving skills with an emphasis on product development.
• Experience using statistical computer languages (R, Python, SQL, etc.) to manipulate data and draw insights from large data sets.
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
• Excellent written and verbal communication skills for coordinating across teams.
• We’re looking for someone with 3-7 years of experience manipulating data sets and building statistical models, has a Master’s or Bachelor’s in Computer Science or another quantitative field, and is familiar with the following software/tools:
• Experience visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3, ggplot, etc.
  Additional (Nice to Have):
• Image Analytics – Computer Vision, Image Processing, Streaming Analytics, Human Machine Interaction.
• Spatial Data base knowledge • GIS software knowledge and experience (preferred ArcGIS and QGIS)
• Deep learning Tensor flow experience of 1 year • Experience with Tensor flow
• Experience with different deep learning algorithms such as CNN, RCNN and MRCNN etc.
• Experience with performance profiling, characterization and optimization
• Knowledge of common machine learning frameworks such as RNN, Pytorch, Caffe.
• Experience working with large datasets and Deep Learning algorithms Skills needed for model usage
• Understanding of the model
• Basic of machine learning and Computer Vision
• Basic of Deep learning (machine learning) – to learn on existing models. Skills needed for model Re-training after
• Good experience in Computer Vision – to learn on existing models.
• Good experience in Deep learning (machine learning) – to learn on existing models”