Job Description

About the job 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 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 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.