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Profile

Data Scientist with 2 years of industry experience and 3 years of research expertise in machine learning, data engineering, and statistical analysis. Proficient in designing and automating ETL pipelines using tools like PySpark and GCP to process large-scale datasets. Skilled in developing predictive models to optimize pricing, promotions, and merchandising strategies. Strong background in statistical methodologies, including hypothesis testing and A/B testing, to derive actionable insights. Experienced in collaborating across cross-functional teams and presenting complex analyses to leadership, driving data-informed business decisions.

Skills
Programming Languages: (Expert: Python, SQL | Experienced: R, JavaScript), Cloud Platforms & Infrastructure: (Expert: Google Cloud Platform (GCP) – Vertex AI, BigQuery | Experienced: Cloud Storage), Data Analysis & Machine Learning: (Expert: Pandas, Numpy, Scikit-Learn, SciPy, NLTK, OpenCV, PySpark | Experienced: PyTorch, TensorFlow, Keras, Hugging Face Transformers | Proficient in Regression, Classification, Clustering, Deep Learning, and NLP techniques.), Visualization & Reporting: (Expert: Plotly, Seaborn, Matplotlib  | Experienced: Power BI, Tableau | Expertise in data-driven storytelling and dashboard development for business stakeholders), Statistical Analysis & Predictive Analytics: (Expert: Hypothesis Testing, A/B Testing, Time Series Analysis, Predictive Analytics/Modeling, Forecasting), Collaboration & Project Management: (Experienced: Agile methodologies, Git, CI/CD pipelines (GitLab), DevOps tools | Skilled in: Cross-functional teamwork across merchandising, operations, and supply chain.), Orchestration & Automation: (Experienced: CI/CD pipelines (GitLab), DevOps tools)
Professional Experience
Senior Analyst - Space Optimization, Loblaw Companies Limited
May 2023 – present | Toronto, Canada
  • •Automated Space Optimization Tools: Led the development of an automated decision support system on GCP using Python, BigQuery, and Vertex AI, providing real-time insights into assortment and risk metrics. Enabled merchandising teams to make agile, data-driven space allocation and inventory adjustments, optimizing store layouts and boosting sales.
  • •ETL Workflow Management: Designed and implemented ETL pipelines for migrating large datasets to GCP, enhancing reporting reliability and supporting agile analytics for space optimization. Improved the accuracy and speed of data-driven decisions across teams.
  • •Performance Reporting Automation: Built a robust automated reporting framework on GCP, delivering timely insights on sales performance and seasonal forecasts. Enabled leadership to optimize promotional strategies, improve space utilization, and enhance cross-functional planning, resulting in efficiency gains.
  • •Operational Efficiency through DMAP Migration: Directed the migration of DMAP workflows from Excel, SAS, and Teradata to GCP, reducing data management efforts by 5-10 hours per planning cycle. Improved space planning and inventory management, enabling effective promotional execution.
  • •Application Development: Contributed to the development of the DMAP APP, creating dashboards to enhance visibility into space utilization and product allocation. Streamlined execution processes and reduced manual work, improving operational efficiency in merchandising strategies.
  • •Mentorship and Leadership: Mentored co-op analysts, fostering a collaborative team culture and supporting skill development in data analysis, space planning, and problem-solving. Enhanced team performance and cohesion, achieving business objectives.
    • Data Scientist - Market Analytics coop, Loblaw Companies Limited
    Sep 2022 – May 2023 | Toronto, Canada
  • •Data Engineering & Market Analytics: Developed and managed ETL workflows on GCP using Python, enabling efficient data pipelines for merchandising and pricing optimization. Leveraged advanced data mining techniques such as regression, classification, and clustering to derive actionable insights supporting assortment planning, promotional performance, and customer segmentation.
  • •Predictive Modeling for Customer Segmentation: Built and deployed predictive models on GCP Vertex AI to analyze 1.2 million customer purchase records, identifying 400,000 loyal customers. Prioritized 96,000 customers for targeted marketing campaigns, improving customer engagement through proximity-based offers and digital touchpoints.
  • •KPI Development for Sales Optimization: Designed custom KPIs, including metrics like basket penetration and frequency, to evaluate product performance. Conducted A/B testing to refine promotional offers, enhancing sales strategies and customer engagement. Collaborated with cross-functional teams to create data-driven strategies that improved space planning and promotional effectiveness.
  • •Analysis of Declining Sales Segments: Performed prescriptive analysis to identify underperforming categories and declining customer segments. Designed targeted promotional strategies that led to a $0.5M increase in incremental sales within the first week of a pilot program. Utilized statistical modeling and advanced analytics to inform decision-making and drive revenue growth.
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    Data science Researcher, Fleming College, NSERC Industrial Research
    May 2022 – Oct 2022 | Toronto, Canada
  • •Predictive Model Development: Built machine learning models to predict infrastructure issues (e.g., water pipe breakages), employing advanced feature engineering and statistical analysis to drive improvements in predictive accuracy, leading to optimized maintenance schedules.
  • •Feature Engineering & Optimization: Integrated external data sources (traffic data) to enhance model accuracy by 7% and improved F1 score by 5%. Applied statistical tests (ANOVA) to ensure the robustness and reliability of features, aligning with best practices in experimental design and hypothesis testing.
  • •Regression & Classification Models: Developed a combined regression and classification model to forecast pipe breakages over a five-year horizon. Improved model performance by increasing R² from 0.37 to 0.87 and reducing RMSE from 0.24 to 0.09, showcasing expertise in both regression and classification techniques.
  • •Data-Driven Insights: Uncovered actionable insights from complex datasets, providing recommendations to improve infrastructure reliability, which aligns with generating business-impacting insights through data analysis.
    • System Analyst, Department of Information Technology, Karafarin Bank
    Apr 2015 – Sep 2015 | Tehran, Iran
  • •Gathered and analyzed system requirements, proposed innovative solutions to the development team, and collaborated with stakeholders to ensure successful project delivery. Created and executed comprehensive test cases to ensure optimal system performance and maintain high-quality standards. Worked collaboratively with the Mobile Banking development team to develop and implement software solutions to meet business needs and improve customer satisfaction.
    • Education
    Post Graduate Certificate in Applied AI Solution Development, George Brown College
    Jan 2022 – Dec 2022 | Toronto, Canada
  • •GPA: 3.86/4 - Dean's list for all semesters
  • •8K UrbanSound Classification: Transformed 8732 labelled sound data to spectrograms and employed ANN, CNN and RNN models with various architectures-achieved 90+ accuracy with all architectures- to detect ambient sound as the basic building block for a noise cancellation application.
  • •Visualization and analysis of Suicide data: created Storyboards, Dashboards and Scoreboards in Tableau, conducted root cause analysis using historical and present data from both UN and WHO, to help prevent suicide or decrease the suicide rate.
  • •Sentiment Analysis of Financial News Headlines: Applied NLTK library for exploratory analysis, built BoW and TF-IDF models for vectorization and benchmarked different classification algorithms.
    • Ph.D. Student in Computer Science, Dalhousie University, NICHE Research Group
    Jan 2021 – Jan 2022 | Halifax, Canada

    Research area: Home-based continuous care using ontology-based context-aware systems.

  • •Designed a hybrid context-aware system for smart homes using LSTM neural networks, Semantic Web technologies and knowledge graphs to support activity recognition, enabling independent living for elderly and chronically ill individuals.
  • •GPA: A-
  • •Interactive Sentiment Analysis and Clustering of Hiking Trails in Nova Scotia: Scrapped Nova Scotia Hiking Trails data from GimmeAlltrails.com to perform text analysis using NLP, and performed LDA modelling on customer reviews. Created trail clusters based on hiking features to provide similar trail recommendations. The analysis was demonstrated using a Dashboard created using the DASH framework.
  • •Automatic eye diseases classification of optical coherence tomography (OCT) images using Deep Neural Networks: Developed a Neural Network model with ResNet framework to distinguish the abnormalities from the normal eye scan and then classify those three diseases with grayscale images. Implemented by applying ResNet18 model and the PyTorch python package.
    • M.Sc. in Computer Engineering, Artificial Intelligence Specialization, Science and Research Branch, Islamic Azad University
    Sep 2015 – Mar 2019 | Tehran, Iran

    Research area: Design and Implementation of a Cognitive Business Strategy Based on Customer Satisfaction.

  • •Developed a cognitive business strategy using Hopfield Neural Networks and ontology modeling to optimize customer satisfaction and loyalty, and proposed a decision support system for real-time performance monitoring.
  • •Other Research Practices: Implemented a brain-computer interface using EEG signal classification with neural networks and developed a social media recommendation system using data mining techniques.
  • •GPA: 4.0/4.0 (17.38/20)
    • B.Sc. in Computer Engineering, University of Science and Culture
    Sep 2010 – Aug 2014 | Tehran, Iran
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