Arpitha L S
Aspiring Data Scientist
Phone No: 819-***-****
Well-equipped for a role where I can utilize my data analytics skills, machine learning algorithms, Natural Language Processing and Deep Learning for solving real time business problems. Education
Narsee Monji Institute of
Management Studies (2020)
International School of
Oct-2018 to Aug-2019
Job Title - Finance Executive • Company Name – Atkins India Jun-2016 to Sep-2018
Job Title - Analyst • Company Name – Oracle India
Sep-2015 to May-2016
Job Title - Accounts & Finance Administrator • Company Name – IBM India A brief summary of my responsibilities handled in the above organizations:
• Liaising with suppliers, business units and Atkins employees to resolve queries
• Maintaining responses received via various databases.
• Preparing weekly reports and monthly reports for the suppliers awaiting payments, accruals.
• Preparing a trend analysis for the different types of invoices flowing in.
• Preparing a Data Analysis on the research done.
• To deal with correspondence sent to shared mailboxes managed by the team in line with agreed service level agreements.
• To progress items in the pending list, delivering exemplary customer service by handling telephone and electronic communications in accordance with agreed process, standards and service levels.
• Identifying rejections and resolving the issue before re-issue of payments.
• Preparation and presentation of training documents.
• To prioritise workload to achieve daily tasks and meet deadlines.
• Audit support services- internal and external support.
• UAT Testing for the new OPC Application.
Data Science Projects
Explored various industries like the healthcare, Marketing, finance, Social Media, banking sectors and IT Industries for my Projects on Data Science. Some of the projects I have worked on include fraud detection, cancer and diabetic patients’ detection and loan defaulters list.
1. Project: Customer Segmentation
Recommending customers new offers and products based on different clusters using K-means clustering. Approach: Picking features based on the historical data, which will be helpful for predicting the customer choices and preferences, based on elbow calculation method to segregate into number of clusters. Verified various cluster centers initialization techniques like random and k means++, using k means clustering technique made customers into different segments based on their age, gender, previous purchase suggested new offers and products. 2. Project: Product review Analysis
Analyzing the reviews of various products using Natural Language Processing techniques and Naive Bayes algorithm. Approach: Loading the data from csv files and data pre-processing techniques like removing special characters and stop words, splitting and lemmatization words using NLTK, created bag of words after textual analysis, which converts keywords into columns, using logistic regression algorithm classified reviews and based on confusion metrics we calculated accuracy of the model. Using k-fold cross validation technique verified over-fitting. 3. Project: Image Classification
To predict which category does the image belong to. Approach: using deep learning framework keras, made use of the Convolutional Neural Network and VGG16 Architecture.