Machine Learning Data Science
Resume:
SKILL SET
• Programming Languages:
Python, C++, VBA, SQL.
• Database platforms:
Database technologies such as MySQL,
MongoDB, SQL Server.
• Data science libraries and platforms:
Pandas, NumPy, Scikit-Learn, Matplotlib,
Seaborn, Bokeh, Power BI, CUDA, Microsoft
Power Platform.
• Computer vision libraries:
TensorFlow, PyTorch, Keras, OpenCV, YOLO,
and UNET (For object detection, image
classification, segmentation models).
• Large Language Models (LLM):
Customize or fine-tune LLMs (e.g., Chat GPT)
models to cater to specific domain
requirements.
• Conventional Machine Learning:
Supervised learning techniques (Linear and
Logistic Regression, Neural Networks, SVM,
Trees), unsupervised learning (k-means,
anomaly detection), ensemble learning
methods (Ada Boost, Gradient Boost, Random
Forest). Deep learning (e.g., GANs, and RNNs).
• Statistical methods in data science
Sampling Techniques (random, stratified,
cluster), Bayesian Statistics, Time Series
Analysis, Regression Analysis (linear &
nonlinear regression, logistic regression,
multivariate regression), Multivariate Analysis
(PCA, factor analysis, cluster analysis).
• Coding Tools & Platforms:
VS Code, Visual Studio Code, Spyder, Jupyter
Notebook, Git, Linux, Azure DevOps, Azure
ML, Docker, & Torch Serve. Cloud platforms
especially Azure for developing, testing,
deploying machine learning models.
• CRM Platforms:
Salesforce
• Decision Management Platforms
OpenRules
• Communication & Reporting:
Effective translation of complex data insights
and results. Professional reports.
Max Faramarzi
Data and ML Engineer
ABOUT ME
Multidisciplinary in computer science and civil engineering. Expertise includes:
• Problem-Solving: Strong analytical skills to tackle complex business challenges with focus on adding values.
• Materials Informatics & Data Science: Data wrangling, visualization, advanced machine learning models for materials design & performance.
• Statistical Analysis & Predictive Modeling: Exploratory data analysis, data curation, feature engineering, anomaly detection, & predictive models.
• Cloud Computing & Data Pipelines: Developing & deploying pipelines & models on Azure.
• Communication & Collaboration: Explaining complex data insights to stakeholders, fostering innovation. Experienced with working in multidisciplinary teams, integrating insights from materials science, chemistry, physics, & data science. WORK EXPERIENCE
Saint-Gobain Omniseals Solution, Bristol, RI
Data Engineer (Jan 2023 – Present)
• Materials Informatics: Led an end-to-end project to advance non-PFAS materials development. Utilized Python for data processing/web development, SQL Server for database management, and Power Platform for visualization. Collaborated closely with materials developers, defining project scope, conducting data mining and cleaning, creating databases, developing visualization dashboards, and training machine learning models.
• Data Mining: Engineered Python and VBA scripts to extract and process dimensions and annotations from CAD drawings, effectively converting them into a structured database.
• Automation Pipelines: Designed and implemented automation pipelines, including the integration of data between Salesforce and SharePoint using Power Automate, enhancing data flow and operational efficiency.
Saint-Gobain Research North America, Northborough, MA Machine Learning Intern - Full stack (Summer 2022)
• Abrasive Materials Imaging: Conducted optical microscopy imaging of Norax Belt and collaborated with SGRNA-CRL team in developing high quality images of Thin Wheels.
• Collaboration for Annotation: Coordinated with third-party vendors to annotate images for preparing high-quality datasets for machine learning model training.
• Computer Vision Development for Defect Detection: Developed and implemented advanced computer vision algorithms using Python libraries, including OpenCV and PyTorch, to automate defect detection in abrasive materials.
• Model Deployment and Optimization: Deployed machine learning models using Docker and TorchServe, ensuring efficient and scalable model performance. Halt International Business School, Boston, MA
NLP & Data mining TA (Winter 2022)
Rhode Island Department of Transportation, Providence, RI Data Analyst Intern (Summer 2019)
University of Rhode Island (URI), Kingston, RI
Software Developer Intern (Summer 2021)
Teacher & Graduate Research Assistant (2017 – 2020) EDUCATION
M.Sc., Computer Science, University of Rhode Island (2020-2022) Specialization: Machine Learning and Database, GPA: 3.9 M.Sc., Civil Engineering, University of Rhode Island (2018-2020) Specialization: ML applications in road damage detection, GPA: 3.9 M.Sc., Civil Engineering, University of Guilan (2012-2014) Research: Design and characterization of carbon nanotube modified asphalt binder and mixture. B.S., Civil Engineering, International University (2006-2011) Location: Watertown, MA
************@*****.***
linkedin.com/in/mfaramarzi
Residency Status: Permanent Resident (Green Card holder)
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