Data Scientist - Machine Learning

Role : Data Science

- Developed analytical solutions to unstructured business problems

- Understanding and translating customer needs into business and technology solutions via the range of leading digital methodologies and/or solutions that drive business value

- Proficient in using machine learning tools and statistical techniques to produce solutions to various problems

Desire Candidate -

- Knowledge and experience with statistical Natural Language Processing (NLP) methods and technologies is desired.

- Experienced in AI / ML projects using languages such as Python, R and Scala.

- Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, Regression, etc.

- Experience with data science toolkits, such as Shiny, Trifacta, NumPy, Pandas, NLTK, Scikit-learn, SpaCy, etc. Excellence in Python is highly desirable.

- Ability to pick up new tools / techniques in ever changing technology landscape is a default expectation.

- An enterprise experience demonstrating all the above is must.

- Experience with big data analytics & Hadoop ecosystem of tools.

- Expertise in leveraging Azure cloud for analytics work loads using data bricks.

- Undertake preprocessing of structured and unstructured data

- Experience of working with AI platform like DataRobot will be helpful.

- Expertise in SQL much needed and ability to work with SQL server environments and exposure to SSIS will be added advantage.

- Experience with data visualization tools, such as Power BI.

- Good understanding and experience of ML modeling lifecycle and pipelines, including data preparation, data wrangling, ML model development, model training, model performance measurement and tuning

- Experience with NoSQL databases, such as MongoDB, Cassandra, HBase and data frameworks, such as Hadoop.

- Good applied statistics skills, such as distributions, statistical testing, regression, etc.

- Experience on deep learning frameworks, like Tensorflow, Keras, etc and experience in dealing with text and/or image data will be desirable.

- Demonstrated knowledge of end-to-end deployment solutions for data products

- Experience with data cleaning, preparation, feature engineering and model selection techniques - Good understanding of the risks and trade-offs involved in choosing different ML solutions

- Ability to seek empirical evidence through proofs of concept, statistical validation and external research - Experience with Cognitive Services on Azure/AWS.