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.