M A Rahman

Experienced Business & Data Analyst with 3 years of expertise, including 1 year in Dubai, specializing in machine learning, statistical analysis, and workflow automation. Proficient in Python, SQL, and Power BI for building dashboards, tracking KPIs, and CRM analytics. Strong in predictive modeling and uncovering data-driven insights. Skilled at optimizing processes and collaborating cross-functionally to deliver impactful solutions that improve operations, guide strategy, and drive business performance.

Education

B.Tech. in Mechanical Engineering

Jawaharlal Nehru Technological University Hyderabad

Mahabubnagar, Telangana

GPA: 75.58/100

Graduation: May 2019

Relevant Courses:

• Data Structures and Algorithms
• Machine Learning & Artificial Intelligence
• Advanced Programming
• Big Data & Distributed Systems
• Statistics & Probability
• Software/Web Development
• Image Processing & Pattern Recognition
• Operating Systems & Networks

Skills:

• Programming Languages: Python, SQL
• Packages: Scikit-learn, TensorFlow, Keras, Pandas, NumPy, Matplotlib, Seaborn, Plotly
• Databases: PostgreSQL, MySQL, ETL, Google Big Query
• Visualization: Tableau, Power BI, Looker Studio
• Frameworks: Streamlit, Flask, RESTful API
• DevOps: Git, GitHub, Azure

Industry Experience

Data Analyst | PDAinfotech | Dubai, UAE

Nov 2024 - Present

Churn & CLV Modeling: Led the design and deployment of churn prediction, customer lifetime value (CLV), and customer segmentation models for a retail client, improving retention by 18% and generating AED 150K in additional quarterly revenue.

Conversion Optimization: Developed and implemented trial-to-paid conversion and churn prediction models for PDAinfotech’s SaaS platform, boosting conversion rates by 25% and reducing churn by 12%.

Automated Reporting: Automated end-to-end reporting pipelines using SQL, Python, and Google Sheets, reducing manual reporting effort by 60% and delivering real-time business insights.

Dashboard Development: Built and maintained interactive dashboards in Power BI to track KPIs across sales, marketing, and customer success, enhancing data-driven decision-making.

Executive Reporting: Reported directly to the C-suite team on daily, weekly, and monthly metrics, providing strategic insights that guided business planning and operational improvements.

Cross-functional Collaboration: Partnered with teams across sales, marketing, and product to translate analytics into actionable strategies, optimizing campaigns, targeting, and adoption.

Data Governance: Improved data quality and governance by designing validation frameworks and standardizing datasets, increasing reporting accuracy and reliability by 30%.

Experimentation & Insights: Conducted A/B testing and campaign analytics for marketing and product initiatives, enabling data-driven optimization and measurable performance improvements.

Data Science Intern | Ai Variant | Hyderabad, India

June 2023 - Aug 2024

Advanced Data Science Applications: Leading the development of innovative data science solutions, focusing on real-time predictive modeling and advanced machine learning algorithms.

Cross-functional Collaboration: Collaborating with data engineers and business stakeholders to deploy models into production, optimizing business processes and driving data-driven decision-making.

Model Optimization: Implementing hyperparameter tuning and model evaluation techniques, leading to a 15% improvement in model accuracy across various projects.

Scalable Solutions: Contributing to the design and deployment of scalable machine learning pipelines using cloud platforms, ensuring robust and efficient data processing.

Capstone Projects: Developed and presented multiple projects, including predictive modeling, time series analysis, and natural language processing applications.

Bussiness Specialist | Amazon | Hyderabad, India

May 2022 - May 2023

Process Automation: Spearheaded the design and implementation of Excel macros, automating seller support and daily operations. Achieved a 25% increase in process efficiency, significantly reducing manual workload.

Documentation & SOPs: Developed and documented the SOP for the transparency project, enabling smooth knowledge transfer and standardized workflows.

Data Management: Leveraged Hubble Query Language and ETL processes to extract, transform, and load data, optimizing data flow and enhancing catalog management accuracy.

Operational Optimization: Played a key role in the Seller Flex and transparency projects by automating routine processes, minimizing manual intervention, and boosting task precision.

Cross-functional Collaboration: Partnered with diverse teams to improve seller support systems, fostering a more streamlined and efficient workflow across departments.

Continuous Improvement: Employed automation tools and data analysis to consistently monitor and enhance cataloging processes, ensuring high standards of data integrity and operational excellence.

Projects

Apple Stock Forecasting

Jan 2024 - Feb 2024

Tools: Python, LSTM, ARIMA, Prophet, Scikit-learn, Plotly

• Developed a time series forecasting model for Apple stock prices, integrating LSTM, ARIMA, and Prophet models, achieving a 15% reduction in RMSE compared to traditional methods.

• Predicted stock trends with 85% accuracy, demonstrating potential for informed investment decisions.

• Innovated by combining multiple forecasting models to enhance prediction robustness.

Bankruptcy Prevention

Mar 2024 - Apr 2024

Tools: Python, Logistic Regression, Random Forest, Gradient Boosting, SVM, Scikit-learn

• Built a predictive model for bankruptcy prevention, reducing false positives by 18% and improving overall accuracy by 22%.

• Demonstrated business value by identifying at-risk firms earlier, allowing for timely intervention.

• Introduced an ensemble approach combining various models, resulting in a more accurate and generalizable solution.

NLP Sentiment & Classification Analysis

May 2024 - Jun 2024

Tools: Python, NLTK, Scikit-learn, Plotly, Selenium, Flask

• Conducted sentiment analysis on Amazon reviews, increasing classification accuracy by 20% through extensive feature engineering and model tuning.

• Enabled companies to better understand customer sentiment, leading to targeted marketing strategies.

• Automated data scraping for real-time analysis, enhancing model responsiveness.

Solar Power Prediction

Jul 2024 - Aug 2024

Tools: Python, Gradient Boosting, XGBoost, Scikit-learn, Streamlit

• Created a predictive model to estimate solar power generation with 88% accuracy, assisting energy companies in optimizing resource allocation and reducing operational costs by 12%.

• Improved accuracy of energy forecasts, leading to better grid management and reduced energy waste.

• Applied advanced feature engineering techniques, improving model performance and providing deeper insights into the key drivers of solar power generation.

Certifications

• Internship Certification - AI Variant Feb 09, 2024 – Present
• Data Science Certification - ExcleR Solutions May 22, 2024
• Masters Program in Data Science - NASSCOM, Government of India Aug 12, 2024
• Machine Learning with Python - IBM Developer Skills Network Jul 24, 2024
• Python 101 for Data Science - IBM Developer Skills Network Jul 25, 2024

Technical Tools

IDE & Tools: Jupyter Notebook, Colab Notebook, VSCode, PyCharm

Additional Experience

Data Structures & Algorithms - C++: Implemented various algorithms to solve complex computational problems during academic projects.

Operating Systems & Networking: Gained foundational knowledge in OS concepts and computer networking, relevant to data science applications.

Interests

Continuous Learning: Staying updated with the latest trends in AI/ML through online courses and workshops.

Open-Source Contribution: Actively contributing to open-source projects in data science.

Langauges:

• Urdu (Native)
• English
• Hindi
• Telugu