Hire Utsav D. - ML Engineer - Bangalore

* Achieved 2% average MoM reduction in chat transfer rate in customer service chatbots & voicebots across 7 different languages * Boosted customer service efficiency by providing agents with suggested responses derived from historical customer interactions * Achieved 65% accuracy in recognizing 34 custom named entities from chat conversations between customer and agent * Utilized Large Language Models (LLMs) to enhance agent performance through chat summary generation and evaluation of response empathy, meaningfulness and resolution quality * Utilized supervised ML models to identify subtopic and intent of customer utterances with average accuracy of 69% across all languages * Elucidated the decision-making process of machine learning models and their performance metrics to non-technical audiences and trained them in data-labeling tools and procedures. * Leading the Machine Learning team comprising of 7 team members

* Key Deliverables: * Achieved 85% accuracy in ML based spend classification for a consumer goods company’s procurement workflow to quantify and improve their savings opportunities * Achieved over 40% reduction in redundant/duplicate data using Locality Sensitive Hashing and Jaccard distance techniques among others to reduce training data labelling effort * Designed and productionized a supplier news analytics module that included news categorization, sentiment analysis and entity recognition using an ensemble of Deep Learning techniques for a proprietary procurement management product * Designed / developed / productionized a proprietary product which extracts & formalizes unstructured information into knowledge representation using Python and Deep Learning models * Key role in improving product efficiency by transforming concepts from Deep Learning research * papers into usable features

* As ML Engineer * Responsible for design & development of an end-to-end machine learning model to predict outcome of new contract proposals for a US based print and digital document seller with baseline accuracy of 80% * Integral role in improving baseline accuracy of the above-mentioned model by 4% using Box Cox transformation, Gradient Boosting algorithm and Voting classifiers from Scikit-Learn * Engaged in creation of an end-to-end natural language processing system to identify and extract relevant portions from a large corpora of customer grievance emails for a Switzerland based insurance provider * Successfully achieved 70% F1-score (5% more than client expectation) using spaCy, NLTK, fastText, LSTM and Python regular expressions * Accomplished 50% faster comprehension of the above-mentioned emails thereby improving the overall turnaround time of the system * Instrumental in implementing a continuous model improvement pipeline in production to track system efficiency; gathered training data over time using qualitative & quantitative methods * Achieved optimum balance between performance, scalability and cost of production deployments of training / inferencing pipelines using both closed source (Azure Machine Learning compute clusters, Azure Kubernetes services) and open source (Docker, fastAPI) technologies * Established trackable and sustainable pipelines of above-mentioned ML models utilising Azure ML, Databricks and ML flow technologies * As BI Developer * Successfully created a business intelligence semantic models using SQL Server Analysis Services (SSAS) Tabular to facilitate advanced analytics on retail data * Involved in migration of 450+ reports to Power BI from legacy systems * Served as a Team leader (comprising of 5 team members), onsite coordinator and SPOC for US and APAC clients