Samar Heydari Email & Phone Number

Tehran, Tehran Province, Iran

Developed, implemented, and optimized statistical predictive modelling and machine learning modelsusing R and Python to predict each vendor’s final price in the Iran Mercantile Exchange steel market.• Implemented statistical and machine learning models predictive model to simulate and cluster marketprices to predict the probable price trends in the Iran Mercantile Exchange steel market, to meetcompany trading strategy with 11% optimization in S&OP.• Successfully drove data-driven decision-making, collaborating with multiple cross-functional teams, toidentify, analyse and solve critical business problems.• Extracted, cleaned, and manipulated data from multiple sources, ensuring 98.9% data integrity andquality of the SQL database leading to better insights.

Tehran, Tehran Province, Iran

• Leveraged advanced analytics to analyze business-related data, segmenting and classifyingcustomers to tailor marketing strategies and designs.• Implemented a chatbot system to enhance customer experience functionalities, dynamically adjustingresponses based on each customer’s class and preferences.

Applied advanced data processing techniques to gather and preprocess neurological data, achieving a30% increase in efficiency and speed.• Developed and implemented classifiers, object detectors, and predictive models, enhancing efficiencyby 23% in data analysis workflows.

• Developing a DeFi solution to monitor wallet allowances and manage the revocation process.• Writing and deploying 5 smart contracts using Solidity, ensuring robustness and security inblockchain-based applications.• Integrating Ethereum blockchain backend with frontend using Web3.js and Ethers.js for seamlesssmart contract interaction.

Iran

• Defined metrics based on eye contact frequency to indicate the engagement of end users with thegamified application.• Deployed and trained ensembled models on the annotated data, within the application to enhance theuser experience of Autistic children giving a 14% rise in time-on-task key performance indicator (KPI).

• Collection and pre-processing of the biological sensory inputs from pregnant persons.• Implement machine learning reinforcement learning models to customize recommendations based oneach person’s biological condition leading to 15% more accurate recommendations.• Pioneered a culture of continuous learning within the data science team, incorporating the latestindustry techniques and tools, improving team productivity by 25%.