I'm Mostafizur Rahman.

In my current role, I work at the intersection of data and business. I analyze client data and market trends to understand where growth opportunities exist and how they align with real customer needs. Alongside market research, I handle lead generation and manage prospects using CRM tools, ensuring that follow-ups and pipelines stay organized and actionable.
I regularly prepare proposals and internal reports that translate data insights into practical recommendations for clients and internal teams. This role has strengthened my ability to connect analytical thinking with business outcomes and communicate insights in a way that supports decision-making.

As a Quality Assurance Associate, I was responsible for validating large-scale AI/ML datasets across image, LiDAR, and sensor data, consistently maintaining over 99% data accuracy. I performed routine data quality checks to identify recurring issues and applied corrective actions to ensure datasets were reliable for downstream analysis and model training.
I worked closely with QA teams to refine annotation guidelines applied across multiple projects, improving labeling consistency during quality reviews. I also contributed to improving quality review workflows and supported basic process automation efforts to make reviews more efficient.

In this role, I worked hands-on with dataset preparation for AI/ML projects, annotating and preparing over 20,000+ images, LiDAR scans, and sensor records while consistently meeting productivity and quality standards.
I collaborated with QA teams to refine annotation guidelines and improve review clarity, which helped reduce inconsistencies during quality checks. This role built my foundation in structured data preparation, attention to detail, and working within production-level AI workflows.

During my internship at Hydroquo+, I collected and analyzed over 3 million sensor data points to identify operational trends and anomalies. I developed interactive dashboards and visual reports that helped internal stakeholders better understand system performance and make more informed decisions.
I also worked with cross-functional teams to improve data collection and reporting processes, gaining practical experience in data analysis, visualization, and quality control in a real-world operational environment.

I earned my Bachelor of Science in Physics, studying courses like Linear Algebra, Calculus, Quantum Mechanics, Computational Physics, Statistics and Statistical Physics. I also gained practical experience using MATLAB for problem-solving and simulations. This foundation sharpened my analytical skills and sparked my interest in data-driven technologies, paving the way for my move into AI and machine learning.

Completed a hands-on program focused on practical skills in Data Science and Machine Learning. Gained experience with Python, SQL, and a variety of data science tools. Covered topics such as data science methodology, data visualization, data analysis, and building machine learning models. Worked on multiple cloud-based labs and assignments, culminating in a Capstone Project that demonstrated real-world application of acquired skills.

Completed a 9-course program covering the core principles of data analysis, with hands-on projects using real-world datasets. Gained practical experience in Excel, SQL, Python, Jupyter Notebooks, Relational Databases, and Cognos Analytics. Developed skills in data manipulation, analysis, visualization, and dashboard creation, equipping me for entry-level roles in data analytics.

Gained an understanding of common careers and industries that rely on Business Intelligence (BI). Explored how data influences strategic decision-making and learned about the key roles BI professionals play within organizations. Developed a basic BI project plan, reinforcing the principles of data-driven business solutions.

Learned how to use Excel and Power BI to collect, manage, and share data for collaborative, data-driven decision-making. Gained practical skills in Data Analysis, Business Intelligence, Business Analytics, Data Import/Export, and Data Analysis Expressions (DAX).

I specialize in data analysis, visualization, and applied machine learning, using hands-on experience from projects and professional work to solve real-world problems. Below is a summary of my core technical skills and confidence levels.

• Python (Data Analysis · ML)
• SQL (Data Querying · Analysis)
• Exploratory Data Analysis (EDA)
• Data Viz (Python · Power BI)
• Machine Learning