In my previous role, I developed an AI-powered recommendation system for an e-commerce platform. The system analyzed user behavior and purchase history to suggest personalized products. This led to a 15% increase in sales and improved customer satisfaction by providing relevant suggestions. The success was measured through A/B testing and user feedback, which highlighted the system's effectiveness in enhancing the shopping experience.

I start by analyzing business processes and identifying areas where manual tasks can be automated or improved using AI. I then collaborate with stakeholders to understand their pain points and gather data to assess feasibility. For example, I once identified a use case for automating invoice processing, which reduced processing time by 40% and improved accuracy.

I am proficient in Python, TensorFlow, and PyTorch for developing machine learning models. I also use tools like Tableau and Power BI for data visualization and AWS or Azure for cloud-based AI solutions. Additionally, I have experience with NLP libraries like spaCy and NLTK for text-based AI applications.

I ensure alignment by involving key stakeholders early in the development process to understand their goals and constraints. I also conduct thorough impact assessments to identify potential risks and ensure compliance with ethical guidelines. For instance, I implemented a fairness assessment for a hiring tool to prevent bias and ensure equitable outcomes.

I have extensive experience in data preprocessing, including handling missing values, normalizing data, and removing outliers. For feature engineering, I create new features based on domain knowledge and statistical analysis to improve model performance. For example, I engineered time-based features for a predictive maintenance model, which increased its accuracy by 20%.