jithesh sunny

data scientist

Infosys Ltd•  February 2018 - January 2022

Fostered collaboration with multifunctional project team members to discuss the action plan and project delivery. Leveraged SQL queries to extract, synthesise, and large volumes of data from data sets and DB. Used matplotlib, and seaborn to visualise data as part of data pre-processing. Integrated the machine learning models with web-based applications through Flask and Django frameworks. • Yielded required outcomes by developing new functions using Python to achieving 20% higher returns compared to previous year. • Automate weekly status reports to save processing time from ~14 hours to ~15 min of the team. • Utilised statistics, data analysis, algorithms and data mining techniques prior to deploying machine learning models. • Developed new python code to reduce ~60% of manual effort on creating quarterly audit risk reports. • Fulfilled data science requirements by effectively prioritising backlogs and managing multiple objectives, interdependencies, and risks. • Coached the junior resources on PEP-standard coding practice/project-specified development standards and actively engaged in Agile practices to cut client churn by 35%. • Led, motivated, and inspired the team of 8 members with a keen focus on talent nurturing through knowledge transfer documentation and comprehensive training/mentoring.

Coventry University

Data Science, MS•  January 2022 - January 2023

Gained in-depth knowledge of core modules, encompassing principles and programming of Data Science, Machine Learning, Information Retrieval, DBMS, Bigdata, and Pyspark. Research Project: Heart disease detection using the Gaussian process. • Planned literature research and established reliable data sources to benchmark the Gaussian classifier model. • Set clear guidelines and permissions for effective data wrangling, utilisation and cleaning. • Developed insight analysis and machine learning algorithms using hyperparameter tuning methods to predict heart disease by 89% accuracy. • Created defined plots using seaborn, matplotlib, and plotly to understand the correct meaning of data. Academic Projects: Vertical Search Engine • Designed a search engine via Flask library to retrieve data from a webpage with the help of the BeautifulSoup library in Python utilising a page ranking algorithm to sort and display the 98% of reliable data. Prediction on House Prices in Boston Suburbs Using Pyspark • Conducted exploratory data analysis on the cleaned data to find the relation between column values. • Predicted pricing and presented output in Tableau dashboard using the supervised learning tools.