
Data Scientist
1 week ago
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.
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