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14/07 Hiral Shah
Branch Manager at Pylon Management Consulting

Views:761 Applications:213 Rec. Actions:Recruiter Actions:109

Manager - Data Scientist (6-9 yrs)

Gurgaon/Gurugram/Noida Job Code: 833864

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.

This job opening was posted long time back. It may not be active. Nor was it removed by the recruiter. Please use your discretion.

Women-friendly workplace:

Maternity and Paternity Benefits

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