Abhishek Kaul

Led end-to-end data science projects, collaborating with cross-functional teams to define project objectives and deliverables. Developed MLops strategies to streamline the end-to-end process of deploying and managing machine learning models in production environments. Worked with Amazon SageMaker, AWS Glue, AWS Lambda, AWS DataBrew to create ML pipelines. Developed and deployed predictive models and algorithms using python to optimize key business processes, resulting in quantifiable outcomes. Utilized machine learning techniques using python to build and train models for customer segmentation, demand forecasting, and recommendation systems. Created interactive data visualizations and dashboards to effectively communicate complex findings to stakeholders. Conducted rigorous model evaluation and performed A/B testing to validate model performance and iterate on improvements. Led review calls with peers, top management, and clients. Proactively held and participated in brainstorming sessions for ML models and algorithms.

Developed and deployed machine learning models for clients across the globe to help them achieve high accuracy in business operations. Conducted data exploration, feature engineering, and pre-processing to enhance model performance and accuracy. Utilized TensorFlow frameworks to build and train deep learning models for image recognition and object detection. Experience with GANs for generating realistic content, such as images and text. Familiarity with Transformers for tasks like language modeling, machine translation, and text generation. Understanding of autoencoders for data compression, feature extraction, and generative tasks.

Collected and analyzed large datasets, identifying patterns, trends, and insights to support business decision-making for nationally distributed sales team. Performed statistical analysis, including hypothesis testing, regression analysis, and time series analysis. Collaborated with cross-functional teams to identify data requirements and provided actionable recommendations based on data insights. Communicated complex findings in a clear and concise manner to both technical and non-technical stakeholders. Led review calls with peers, top management, and clients.

Developed and documented changes as required on the website. Created, updated, and maintained computer programs or software packages to handle specific jobs such as storing or retrieving data, or controlling other equipment. Conducted office automation feasibility studies, including workflow analysis, space design, and cost comparison analysis.