Data Scientist

Role Description
We are looking for a Data Scientist who will support our internal teams with insights gained from analyzing company data. The ideal candidate is adept at using large data sets to find opportunities for product and process optimization and using models to test the effectiveness of different courses of action. They must have strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building, and implementing models, using/creating algorithms, and creating/running simulations. They must have a proven ability to drive business results with their data-based insights. They must be comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.
Essential Job Responsibilities
Strong understanding of NLP techniques including text classification, sentiment analysis, named entity recognition, topic modeling, keyword extraction, semantic similarity, and text embeddings.
Hands-on experience with LLMs, prompt engineering, Retrieval-Augmented Generation (RAG), vector databases, embeddings, and evaluation of LLM outputs.
Experience working with Transformer-based models and frameworks such as Hugging Face, LangChain/LlamaIndex, or equivalent technologies.
Ability to evaluate and optimize NLP/LLM solutions using appropriate accuracy, relevance, hallucination, latency, and business-performance metrics, with an understanding of model limitations and responsible AI practices.
Qualifications
· Graduation in Engineering, Math’s, or Statistics from reputed University
· 1-4 years of experience in building predictive models, recommendation systems, or NLP/text mining tools. Prior experience in NLP is highly preferable
· Proficiency in Statistics and Machine learning. literature: Linear algebra, Matrix factorization, linear and non-linear feature transformation, Linear/Nonlinear Modeling, Un/Semi/Supervised modeling, High dimensional methods.
· Familiar with the foundational approaches to the major data science disciplines, such as data preparation, advanced statistics, machine learning, simulation, and natural language processing
· Experience and proficiency with various programming languages (e.g., Python), machine learning tools (e.g., scikit-learn), statistical packages (e.g., SciPy)

Work arrangement
Yes