Looking for candidates with 6.5 - 8.5 years of professional experience involving technology-focused process improvements, transformations, and/or system implementations
Minimum Years of Experience:6 year(s)
Preferred Qualifications:
Degree Preferred:
Master Degree
Preferred Fields of Study:
Business Analytics, Computer and Information Science, Mathematics
Preferred Knowledge / Skills:
- Demonstrates thorough knowledge and/or a proven record of success in the following areas:
- Understanding new technology learning and quickly evaluating their technical and commercial viability
- Understanding machine learning techniques for addressing a variety of problems (e.g. consumer segmentation, revenue forecasting, image classification, etc.); and,
- Understanding machine learning algorithms (e.g. k-nearest neighbors, random forests, ensemble methods, deep neural networks, etc.) and when it is appropriate to use each technique.
- Demonstrates thorough abilities and/or a proven record of success as a team leader including the following areas:
- Understanding new technology learning and quickly evaluating their technical and commercial viability;
- Understanding machine learning techniques for addressing a variety of problems (e.g. consumer segmentation, revenue forecasting, image classification, etc.); and,
- Understanding machine learning algorithms (e.g. k-nearest neighbors, random forests, ensemble methods, deep neural networks, etc.) and when it is appropriate to use each technique.
- Understanding new technology learning and quickly evaluating their technical and commercial viability
- Understanding machine learning techniques for addressing a variety of problems ( e.g. consumer segmentation, revenue forecasting, image classification, etc.)
- Understanding machine learning algorithms (e.g. k-nearest neighbors, random forests, ensemble methods, deep neural networks, etc.) and when it is appropriate to use each technique
- Building machine learning models and systems, interpreting their output, and communicating the results;
- Moving models from development to production; and,
- Conducting research in a lab and publishing work. Demonstrates thorough abilities and/or a proven record of success with a subset of the following technologies:
- Understanding new technology learning and quickly evaluating their technical and commercial viability;
- Understanding machine learning techniques for addressing a variety of problems (e.g. consumer segmentation, revenue forecasting, image classification, etc.); and,
- Understanding machine learning algorithms (e.g. k-nearest neighbors, random forests, ensemble methods, deep neural networks, etc.) and when it is appropriate to use each technique.
- Understanding new technology learning and quickly evaluating their technical and commercial viability
- Understanding machine learning techniques for addressing a variety of problems (e.g. consumer segmentation, revenue forecasting, image classification, etc.)
- Understanding machine learning algorithms (e.g. k-nearest neighbors, random forests, ensemble methods, deep neural networks, etc.) and when it is appropriate to use each technique;
- Building machine learning models and systems, interpreting their output, and communicating the results;
- Moving models from development to production; and,
- Conducting research in a lab and publishing work.
- Programming including Python, R, Java, JavaScript, C++, Unix Hardware, sensors, robotics, GPU enabled machine learning, FPGAs, and Raspberry Pis, etc.
- Data Storage Technologies including SQL, NoSQL, Hadoop, cloud-based databases such as GCP BigQuery, and different storage formats (e.g. Parquet, etc.)
- Data Processing Tools including Python (Numpy, Pandas, etc.), Spark, and cloud-based solutions such as GCP DataFlow
- Machine Learning Libraries including Python (scikit-learn, genism, etc.), TensorFlow, Keras, PyTorch, and Spark MLlib
- Understanding of NLP and text based extraction. NLP libraries including spaCy, NLTK, gensim etc.
- Visualization including Python (Matplotlib, Seaborn, bokeh, etc.), and JavaScript (d3); and,
- Productionization and containerization technologies including GitHub, Flask, Docker, and Kubernetes.
Required Candidate profile
- Candidates must have overall and relevant 6.5-8.5 yrs of strong exp as a data scientist and should be comfortable for an Individual contributor role.
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