Data Scientist

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 using

transformer-based model

· Experience in creating statistical models and/or optimization frameworks for improving

processes/products/profits

· Expertise with 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 and

un-supervised tasks.

· Tech savy and willing to work with open-Source Tools

· Good to have foundational knowledge on Cloud, API frameworks like Flask, Fast API

· Prior experience working on Mobility or Healthcare domain will be a plus

Mandatory Skills:

· Develop, test, and deploy Machine Learning models using state-of-the-art algorithms with a strong

focus on language models.

· Good knowledge of advanced statistical methods. Mine and analyze data, applying statistical

methods as necessary, pertaining to customers discovery, and viewing experiences to identify

critical product insights.

· Experience in creating statistical models and/or optimization frameworks for improving

processes/products/profits

· Interact with our research team and with key partners in the market to build end-to-end AI/ML

solutions: Conversational AI, document understanding

· Mine and analyze data, applying statistical methods as necessary, pertaining to customers

discovery, and viewing experiences to identify critical product insights.

· Drive efforts to enable product and engineering leaders to share your knowledge and insights

through clear and concise communication, education, and data visualization.

· Translate analytic insights into concrete, actionable recommendations for business or product

improvement.

· Build and improve reusable tools & modelling pipelines and support knowledge sharing across

several teams.