Power Bi Data Science

Chaitanya Sunil Kulkarni

+1-480-***-**** **************@*****.*** https://www.linkedin.com/in/chaitanyasunil/ T E C H N I C A L S K I L L S

Python - NumPy, Pandas, Sklearn, OpenCV, NLTK, Hugging face, Spacy, TensorFlow, Keras, Matplotlib, Seaborn, XGBoost Cloud Technologies - Azure {OpenAI, ML Studio, AI Services, Data Factory, Databricks}, AWS {EC2, Lambda, Athena, Glue, SageMaker} Databases - MySQL, PostgreSQL, Spark SQL

Tool and Frameworks- Git, Docker, Power BI, PySpark, Flask, Statistics, Apache Spark, A/B testing, Apache Kafka, STORY TELLING W O R K E X P E R I E N C E

Data Science Engineer

Qlik Technologies, KOP, Pennsylvania May 2024 - Present

• Developed a search platform using python on an AWS EC2 instance utilizing DBSCAN and Recursive Clustering on vectorized text data which increased the efficiency by 10%.

• Processed over 1 million data points by employing advanced sampling and merging strategies to ensure precise clustering outcomes.

• Integrated LLAMA 3.1 to streamline duplicate case detection through user prompts, further enhancing accuracy and efficiency by 2% in post-clustering analysis.

• Designed and led a training program for 20+ TSEs, equipping them with ML and Gen AI knowledge to become Qlik AI/ML product SMEs.

ML/AI Engineer

Fluke Corporation, Everett, Washington July 2023 – May 2024

• Designed and developed comprehensive chatbot using RAG technology for private data on Azure, leveraging GPT 4, Azure AI search, and created Power BI dashboard showcasing KPIs, resulting in 5x improved productivity for users.

• Orchestrated LLM Ops to automate data ingestion, Indexing and Monitor various metrics; co-authored a comprehensive MLops playbook, setting clear project protocols and standards for future ML projects at Fluke.

• Spearheaded cross-functional collaboration within Fluke Commerce team to successfully deliver multiple GenAI and automation projects improving the efficiency by 80 %, engaging stakeholders to define requirements, develop solution architectures, and oversee end-to-end project development.

• Led project management for multiple projects, overseeing task tracking, documentation, and progress, while developing a playbook for project managers that outlined key elements of GenAI/ML project development. P R O J E C T S

Advanced Energy Demand Forecasting September – December 2023

• Developed an advanced energy demand forecasting model using ProphetFB, ARIMA, and LSTM to predict short- and long-term energy consumption trends, leveraging PySpark for efficient data processing and handling large datasets.

• Conducted extensive data analysis, including trend decomposition, seasonality patterns, and anomaly detection, to identify key drivers of energy demand, with external variables like weather conditions, holidays, and economic indicators incorporated for enhanced accuracy.

• Deployed the forecasting model in a scalable, containerized environment using Docker to ensure seamless integration and reproducibility of the analysis across different systems and environments. Parking spot detector and counter using OpenCV November – December 2023

• Designed and implemented a real-time parking spot detection system utilizing Computer Vision and a tailored ML model. Successfully identified unoccupied parking spots through real time video processing and masking approach, enhancing model accuracy.

• Optimized computational efficiency by 95x by employing a frame processing at an interval of 15 seconds and selectively computing status updates solely for active areas, leveraging pixel differential analysis to discern changes and optimize processing. E D U C A T I O N

Masters in Computer Science August 2022-May 2024

Arizona State University, Tempe, AZ, USA GPA – 4 / 4 Courses – Data Mining, Topics in NLP, Statistical ML, Data Processing at Scale, Fundamentals of Statistical learning Bachelors in Engineering in Electronics and Communication Engineering August 2018 – June 2022 Visvesvaraya Technological University (DSCE), Bengaluru, India

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