Aug 2023 – Present · 2 yrs 10 mos · Remote

• Key Services & Expertise:
• Data Cleaning & Preparation: Ensuring high data quality through rigorous validation and transformation techniques.
• Data Integration: Combining data from multiple sources and platforms to create centralized, reliable datasets.
• Advanced Data Modeling: Using DAX, Power Query (M), and Python libraries to build scalable, insight-rich data models.
• Custom Power BI Dashboards: Designing intuitive, interactive dashboards that clarify complex information and accelerate decision-making.
• Automated Reporting Solutions: Implementing scheduled and dynamic reporting in both Power BI and Excel, reducing manual effort.
• Python & Django Web Apps: Developing web-based solutions for real-time data analysis, reporting, and process automation.
• Predictive Analytics & Machine Learning: Applying statistical models and ML techniques (scikit-learn, TensorFlow) to forecast trends and identify opportunities.
• ETL & Data Pipeline Automation: Building efficient extraction, transformation, and loading workflows for continuous data availability.
• Database Design & Optimization: Structuring and fine-tuning databases (SQL Server, MySQL, PostgreSQL) for improved performance.

Dec 2022 – Dec 2023 · 1 yr · Ahmedabad, Gujarat, India · On-site

• ◦ Leading a diverse team of 8 professionals in developing and maintaining a web-based data processing product for lead generation and marketing consulting.
• ◦ Designed and implemented a comprehensive data mining and preprocessing system, optimizing data quality and efficiency while meeting client needs.
• ◦ Established and managed a data warehousing solution, ensuring seamless data storage and retrieval for timely decision-making.
• ◦ Collaborated with clients and stakeholders to translate business requirements into product features, using agile methodologies for efficient development.
• ◦ Oversaw user interface development and maintained product documentation, contributing to user-friendly interactions and efficient utilization.
• ◦ Presented data-driven insights to clients and key stakeholders using PowerBI, facilitating informed decision-making.

Apr 2022 – Nov 2022 · 7 mos · Udaipur, Rajasthan, India · On-site

• Designed and deployed a comprehensive ITMS system to monitor fleet activities, encompassing real-time driver and vehicle tracking, notifications for incidents, and delivery schedule optimization.
• Utilized cutting-edge GPS and telematics technology to ensure precise fleet tracking and route optimization, enhancing operational efficiency.
• Enhanced safety and security by implementing real-time notification systems for immediate responses to emergencies or unexpected events.
• Conducted thorough fleet and driver productivity analysis, leveraging data insights for performance enhancements and cost reduction strategies.
• Led the creation of a delivery schedule planning module within the ITMS, resulting in improved routing, minimized delays, and reduced operational expenses.
• Collaborated cross-functionally, oversaw system enhancements, and provided user support to optimize the ITMS’s efficiency, scalability, and data-driven decision-making capabilities, leading to significant operational improvements and cost savings.

Aug 2021 – Mar 2022 · 7 mos · Udaipur, Rajasthan, India · On-site

• Developed and deployed a real-time Power BI dashboard to analyze four critical KPIs, including Fill Factor, Turnaround Time (TAT), Trips, and Idle Hours, effectively enhancing transparency and decision-making for stakeholders and clients.
• Achieved an 8% increase in Fill Factor for trucks, resulting in improved fleet productivity.
• Increased ore hauling by 13,230 metric tonnes per month, leading to a net EBITDA gain of $11.91 million.
• Reduced idle hours for trucks resulting in a decrease in fuel consumption worth $8.4 million.
• Proactively engaged with stakeholders and clients, effectively communicating project progress, data-driven insights, and ensuring alignment with business objectives.

May 2020 – Jul 2020 · 2 mos · Chennai, Tamil Nadu, India

• Developed and deployed an Abstractive Text Summarization Model to generate a concise summary of long paragraphs.
• The model was trained on Amazon Fine Food Reviews having 500k observations, using Sequence to Sequence model.
• Implemented custom third-party Attention layer during model building. Reduces human effort and time by 40%.

Apr 2020 – May 2020 · 1 mo · Kharagpur, West Bengal, India

• Created a virtual information assistant by exercising Rasa to let the user know the condition of a region in this current pandemic.
• The used dataset had district wise cases for every state, and the dataset was taken from the government website.
• gives the actual number of active cases in any specific region and also classifies it into a zone(red, green, orange) of the region.

Jun 2019 – Jul 2019 · 1 mo · Kharagpur, West Bengal, India

• Developed a recommender system using Amazon Product reviews having 7.82 million observations and 4 features that suggest
• new items to the user based on their viewing history and according to the user's preferences.
• used Memory-Based and Model-Based approach, Model was build by applying item-item Collaborative Filtering.

May 2019 – Jun 2019 · 1 mo · Kharagpur, West Bengal, India

• Developed a Multilingual Toxic Comment classifier using Deep NLP to identify toxicity and unhealthiness in online conversations.
• Applied pre-trained GloVe word embedding to create embedding matrices.
• Trained the model on a dataset having 224k observations with 12 parameters, Model was build using LSTM and Tensorflow.
• Optimized the classifier by increasing the accuracy from 71% achieved by SimpleRNN to 96% by using the LSTM model.

Apr 2020 – Jun 2020 · 2 mos · Thane, Maharashtra, India

• ◦ Developed and deployed an Artificial Neural Network-based prediction model to assess loan returnability for customers, reducing the company’s risk by 35%.
• ◦ Conducted extensive data preprocessing and feature engineering to prepare the dataset for model training, including data cleaning, normalization, and handling missing values.
• ◦ Optimized the loan prediction model, achieving a 91% accuracy rate compared to the 59% accuracy rate previously achieved using the SVM model.
• ◦ Collaborated with cross-functional teams, including data engineers and software developers, to ensure successful model and API deployment, conducting extensive testing and quality assurance.
• ◦ Implemented continuous monitoring and performance tracking of the deployed model to ensure its accuracy and effectiveness over time.
• ◦ Presented findings and recommendations to the management team through visualizations and reports, translating technical insights into actionable business strategies.