Utsav Datta Email & Phone Number

Bengaluru, Karnataka, India

• 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

Bengaluru, Karnataka, India

• 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 researchpapers into usable features

Kolkata Area, India

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 technologiesAs 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

Kolkata Area, India

Key Deliverables:• Developed Oracle PL/SQL packages procedures for ETL of telecom inventory data from legacy source systems to a modern data warehouse to support better service assurance

Master Of Data Science, Data Science, 74.4

Coursework: Deep Learning, Natural Language Processing, Machine Learning and Data Mining, Computational Statistical Methods

B.Tech In Computer Science And Engineering, Computer Science, 8.23