Solution Architect & Execution Manager

Design and implementation of HR automation solutions

• Designing and architecting Power Platform solutions and reporting solutions (Tableau) for seamless HR automation
• Developing comprehensive solution designs, including system architecture, integration strategies, and data migration plans
• Implementing the specific automation and reporting solutions

Crystall Ball

• Performing time series analysis to predict trends and patterns in sales data of specific food products
• Preparing and cleaning data, including handling missing values and normalizing data to ensure accurate analysis results
• Using Python for modeling and analysis, including libraries like Pandas, NumPy, Scikit-learn, and Statsmodels
• Creating visualizations to present analysis results and forecasts to stakeholders using tools like Matplotlib, Seaborn, and Power BI
• Presenting analysis results and forecasts to management and other stakeholders

Introduction and migration to D365 CRM (Sales & Customer Service)

• Configuring and customizing D365 CRM, including creating entities, forms, workflows, and reports
• Developing and integrating plugins, custom workflows, and web resources with C#, JavaScript, and other relevant technologies
• Using the Power Platform, especially Power Automate, to automate and optimize business processes within D365 CRM
• Performing data migration and integration activities between D365 CRM and other systems using tools like Azure Data Factory and SSIS
• Participating in project planning, estimation, and risk management activities to ensure successful project delivery

Machine learning week

• Develop a detailed weekly plan with clear learning goals and milestones for the Machine Learning week.
• Teach basic machine learning concepts, including algorithms, data preprocessing, and model evaluation.
• Run practical workshops and hands-on sessions on applying machine learning techniques with tools like Python, Jupyter Notebooks, and scikit-learn.

Data science trainee days

• Develop a detailed weekly plan with clear learning goals and milestones for the machine learning week.
• Teach basic machine learning concepts, including algorithms, data preprocessing, and model evaluation.
• Run practical workshops and hands-on sessions on applying machine learning techniques using tools like Python, Jupyter Notebooks, and scikit-learn.
• Introduce advanced topics like neural networks, deep learning, and reinforcement learning.

Fraud Ad Detection

• Designed and implemented machine learning models to identify and classify fraudulent ads.
• Used UI Path to automate data collection and extraction from relevant ad platforms and websites.
• Trained and optimized the models to improve accuracy and reduce false positives in fraud ad detection.

Transport Risk Identification

• Defined and communicated the product vision and created the roadmap to achieve project goals.
• Designed and built user interfaces and experiences using Figma.
• Implemented responsive and interactive frontend components using Angular.
• Designed and implemented RESTful APIs for communication between frontend and backend.

Community-Based Open Source Extensions

• Developed and managed server-side logic and databases using technologies like Ruby on Rails and PHP.
• Supported and maintained open source projects through regular updates, improvements, and responding to user questions.