I’m a data scientist passionate about transforming raw data into meaningful insights that drive innovation, efficiency, and informed decision-making. My work blends machine learning, data analytics, and business understanding to uncover patterns, automate processes, and deliver intelligent, scalable solutions. I focus on solving practical problems through predictive modelling, data-driven automation, and clear data communication. With strong expertise in Python, SQL, machine learning algorithms, and data visualisation tools, I translate complex data into impactful outcomes across various domains. I’ve contributed to projects in finance, healthcare, and technology, building systems for automated data extraction, developing AI-based classification models, and analysing large datasets to improve customer experience and streamline operations. Whether it’s identifying anomalies, optimising strategies, or enabling better decisions through data, I take pride in making data work harder and smarter.

I played a pivotal role in designing, developing, and implementing tailored solutions for enterprise clients, primarily in the banking and finance sectors. Working closely with Emirates Bank of Dubai, Raiffeisen Bank, and other financial institutions, I delivered high-impact integrations that enhanced customer engagement, security, and operational efficiency.

Key Contributions:

• End-to-End Solution Design: I architected and deployed customized workflows, API integrations, and SDK implementations, enabling the seamless adoption of Moxo’s collaboration platform within client infrastructures.

• Technical Consultation & Pre-Sales Support: Worked alongside business and product teams, translating complex client requirements into scalable, high-performance solutions.

• Client-Facing Development & Integration: Leveraged Python, SQL, and Android Studio to build and optimize tailored experiences, ensuring smooth deployment and enhanced user experience.

• Strategic Advisory & Training: Provided technical training and documentation, guiding client teams through integration best practices and post-deployment enhancements.

• Issue Resolution & Optimization: Diagnosed and resolved critical technical challenges, ensuring system stability and maximum uptime for enterprise clients.

This role allowed me to bridge the gap between technology and business needs, showcasing my ability to deliver scalable, data-driven solutions that drive efficiency and impact.

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It’s here where I developed expertise in machine learning, big data analytics, and AI-driven solutions. My coursework covered key areas such as supervised and unsupervised learning, deep learning, statistical modeling, and data engineering. Throughout the program, I worked on multiple projects that applied cutting-edge data science techniques to real-world challenges.

For my dissertation, I developed a system for automated data extraction from PDFs, leveraging OCR, natural language processing (NLP), and machine learning to transform unstructured documents into structured datasets. This work addressed the growing need for efficient, high-accuracy data processing tools, demonstrating real-world applications in research, finance, and business intelligence.

Additionally, I contributed to various projects, including:

• Diabetes Prediction Model: Built and optimized Random Forest, Logistic Regression, and Neural Networks to classify diabetes patients using a dataset of 253,680 records.

• Big Data Twitter Sentiment Analysis: Analyzed millions of tweets from the 2020 U.S. Elections using Spark MLlib and NLP models, enhancing the accuracy and scalability of sentiment prediction.

• Dengue Outbreak Forecasting: Utilized climate and environmental data to predict dengue cases, applying regression models and SHAP analysis to identify key risk factors.

Built a shipment delivery time prediction model, leveraging a dataset of 1,08,338 records with 24 attributes to optimize logistics operations for e-commerce. I applied Random Forest, XGBoost, and Decision Tree algorithms through extensive feature engineering and model experimentation, successfully improving delivery time estimations. The project provided valuable business insights into delivery delays, customer experience, and operational efficiency, reinforcing my ability to translate data-driven solutions into real-world impact.

I engineered a custom-built development board that combined the power of two microcontrollers, addressing critical limitations in widely used development boards. This bespoke solution enhanced processing efficiency, improved scalability, and provided greater flexibility for embedded system applications.

The project’s impact was recognized at an IEEE conference hosted by Sri Siddhartha Institute of Technology, Tumkur, in December 2021, where I presented my research on how this innovation could streamline prototyping and real-world hardware integration.

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Harish Mohan

Data Scientist | Solution Architect

Solution Architect

Product Engineer

Experience

Contacts

Education

Masters in Data Science

PGP in Data Science and Engineering

Bachelors in Electronics and Communication

​Harish Mohan