About this role
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.