Sandeep Mande
Development
Maharashtra, India
Skills
Data Science
About
SANDEEP MANDE's skills align with IT R&D Professionals (Information and Communication Technology). SANDEEP also has skills associated with Customer Service Personnel (Administration and Customer Service). SANDEEP MANDE appears to be a low-to-mid level candidate, with 2 years of experience.
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Work Experience
Machine Learning Engineer
Infosys
January 2022 - February 2024
- Conducted data collection, cleaning, processing, and analysis using Python, SQL, and other tools to extract actionable insights and generate reports for stakeholders. Applied statistical and machine learning techniques such as regression, classification, and clustering to develop predictive models and optimize business processes. Collaborated with cross-functional teams to understand data needs and requirements, ensuring alignment with business objectives. Contributed to the development and deployment of data solutions and applications, including AI model development for embedded, real-time, edge, and cloud environments. Utilized Python and relevant ML libraries to develop and deploy machine learning models using CI-CD pipeline. Presented findings and recommendations to stakeholders and clients, facilitating datadriven decision-making processes. Led the definition of functional and technical specifications for LLM integration, ensuring alignment with user needs and expectations. Collaborated with cross-functional teams to build and maintain LLM solutions, emphasizing sustainable quality and innovation. Projects: Developed an LLM-based chatbot system to enhance customer support services, resulting in a 20% increase in user satisfaction. Fraud Claims Detection: Developed a fraud detection system for insurance claims using anomaly detection techniques and machine learning algorithms. Achieved a 90% detection accuracy rate, significantly reducing financial losses and enhancing fraud prevention measures. Sentiment Analysis: Conducted sentiment analysis on customer feedback data using NLP techniques and sentiment analysis algorithms. Categorized customer sentiments as positive, negative, or neutral, providing valuable insights for product improvement and enhancing customer satisfaction initiatives.
Education
