Shashwat Gupta Email and Phone Number

Helping business achieve ambitious transformation goals.Building data and machine learning services for UK market

- Prepared SOPs for recruitment and business development- Led negotiations with the sponsors- Led a team of >30 junior members- Actively involved in the annual budgeting exercise- Negotiated with key stakeholders

- Closed sponsorship deals worth >INR 2Lakh- Envisaged and conducted India's first consulting conclave in the Metaverse platform- Hosted Aarohan, a fireside chat with industry leaders from the consulting domain

- Product manager for Instaclinik product- Helped perform market study, identify pain points and generate customer insights- Acted on customer feedback to help build a customer centric SaaS platform- Performed Alpha and Beta testing of the product- Launched the product in the market and gathered post-launch feedback

Assisted the CMO in identifying potential targets for M&A in the Healthcare BPO Industry

• Conceptualized and launched NetDiagnostics Python agent application instrumentation tool for 7 potential customers• Spearheaded a 6-member cross-functional team to develop a Python application interceptor tool• Influenced key stakeholders of tool feasibility through detailed competitive analysis and market research• Conceived roadmap to roll out MVP within 6 months by reusing core components & creating data exchange protocols.• Mentored 4 interns during their training; helped… Show more • Conceptualized and launched NetDiagnostics Python agent application instrumentation tool for 7 potential customers• Spearheaded a 6-member cross-functional team to develop a Python application interceptor tool• Influenced key stakeholders of tool feasibility through detailed competitive analysis and market research• Conceived roadmap to roll out MVP within 6 months by reusing core components & creating data exchange protocols.• Mentored 4 interns during their training; helped 2 of them secure PPO’s at Cavisson Show less

• Envision and release AI-powered root cause analysis tool in collaboration with to monitor 2M+ Business Transactions per day • Drove product adoption with a Fortune-200 customer through technical engagements on RCA tool adoption• Minimized development costs by promoting infrastructure optimization for Python agent instrumentation tool• Identified market opportunity by understanding customer pain points through enterprise business connects (EBC)

Log Analysis Tool - AI-powered anomaly detection tool used to monitor logs, identify anomalous logs and anomalous system behaviour• Partnered with 4 internal product teams to develop a prototype of the industry’s first generic log parsing and analysis system• Performed data analysis to define product meta-data schema by using core ML concepts like Principal Component Analysis• Achieved scalability by revamping tool to incrementally train and adapt to latest client release without… Show more Log Analysis Tool - AI-powered anomaly detection tool used to monitor logs, identify anomalous logs and anomalous system behaviour• Partnered with 4 internal product teams to develop a prototype of the industry’s first generic log parsing and analysis system• Performed data analysis to define product meta-data schema by using core ML concepts like Principal Component Analysis• Achieved scalability by revamping tool to incrementally train and adapt to latest client release without any manual intervention• Defined end-to-end user experience to automate error handling & mitigation; projected to save 540+ man-hours/week• End to end ownership from conceptualization to development of the tool with 83% precision in anomaly detection• Proposed the inclusion of automated drill-down report generation that enables the client to swiftly take remedial action• Promoted automated test practices to hold high-quality delivery bar; reducing post-implementation product bugs by ~ 35% Show less

Developed Aspect Based Sentiment analysis system using Un-supervised and Supervised approaches. This was a learning project. I had created systems to find the sentiment polarity using GD-Pairs , CRF - Feature extraction ( naive bayes ) and Deep Learning

A semantic search engine is an improvised version of the traditional cosine based search engine where the cosine score is calculated using a query vector and another vector which constitutes of the tf-idf values for those words in the word-document table.A semantic search engine is based on the concept of Latent semantic analysis. LSA is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of documents and the terms… Show more A semantic search engine is an improvised version of the traditional cosine based search engine where the cosine score is calculated using a query vector and another vector which constitutes of the tf-idf values for those words in the word-document table.A semantic search engine is based on the concept of Latent semantic analysis. LSA is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms.Semantic search engines are more accurate since they not only return exact matches but also return semantically similar words such as cars and automobiles.The search engine was built on R studio as a shiny web app. It has the functionality of returning top 10 results from the total number of documents in the corpus.Search engines have evolved since returning hard-match to the use of tf-idf values which was later modified because of exploitation by SEO experts. Cosine score matching and Semantic analysis is now used to return genuine and related documents.The documents were loaded from a CSV file into an R data frame. A posting list was created after removing stopwords and cleaning the Data. The count, tf , idf values were calculated using mathematical formulas. A term-Document occurrence matrix was made. Query is taken from the user and tf, idf, tf-idf values are calculated dynamically by the server code of the shiny app.LSA is implemented using in-built function which uses the tf-idf matrix. The returned matrix is then converted into text matrix. Query and LSA vector are created and then cosine function is used to match the query with the documents. Corresponding document numbers along wih a summary of the document are also returned to the UI element of the shiny App which displays the data as a data table output. Show less