Education and Work Experience Requirements :
- 5 to 9 years of experience as Data Scientist
- Proven track record and experience with statistical modeling / data mining algorithms such as
o Multivariate Regression, Logistic Regression, clustering algorithms, Support Vector Machines,
Decision Trees etc
o Machine learning, deep learning, graph mining.
o DOE, Forecasting, Segmentation, Uncertainty Analysis etc.
o Data Mining i.e. Text Mining, Classification Methods SVM, NN, etc
o Vector Space model for Unstructured Text
o Sentiment Analysis, Association Mining, Semantic Analysis
Good knowledge of advanced statistical methods. Experience working with Text Data usingtransformer-based model
Experience in creating statistical models and / or optimization frameworks for improvingprocesses / products / profits
Expertise with one of the following scripting languages :o Python, R, Tensorflow, Keras, Pytorch
o Scikit-learn, WordNet, NLTK, SpaCy, Gensim, Large Language Models, Knowledge Graphs
Good and experience of machine learning algorithms and ability to apply them in supervised andun-supervised tasks.
Tech savy and willing to work with open-Source ToolsGood to have foundational knowledge on Cloud, API frameworks like Flask, Fast APIPrior experience working on Mobility or Healthcare domain will be a plusMandatory Skills :
Develop, test, and deploy Machine Learning models using state-of-the-art algorithms with a strongfocus on language models.
Good knowledge of advanced statistical methods. Mine and analyze data, applying statisticalmethods as necessary, pertaining to customers discovery, and viewing experiences to identify
critical product insights.
Experience in creating statistical models and / or optimization frameworks for improvingprocesses / products / profits
Interact with our research team and with key partners in the market to build end-to-end AI / MLsolutions : Conversational AI, document understanding
Mine and analyze data, applying statistical methods as necessary, pertaining to customersdiscovery, and viewing experiences to identify critical product insights.
Drive efforts to enable product and engineering leaders to share your knowledge and insightsthrough clear and concise communication, education, and data visualization.
Translate analytic insights into concrete, actionable recommendations for business or productimprovement.
Build and improve reusable tools & modelling pipelines and support knowledge sharing acrossseveral teams.