Vikram Ardham, Data Scientist and Developer in Ottawa, ON, Canada
Vikram Ardham

Data Scientist and Developer in Ottawa, ON, Canada

Member since November 28, 2022
Vikram loves using his problem-solving skills and technical expertise to solve business problems. His primary skills are translating a business problem into a data problem and solving it using the most efficient computational tools, such as AI or linear regression. Vikram is committed to designing user-centric experiences and is passionate about working with teams whose vision aligns with his values.
Vikram is now available for hire


  • Great Learning and Others
    Python, Artificial Intelligence (AI), Machine Learning, Mentorship...
  • Noibu
    Rust, Python 3, Docker, Amazon Web Services (AWS)...



Ottawa, ON, Canada



Preferred Environment

Visual Studio Code (VS Code), Python 3, Scikit-learn, Docker, Amazon Web Services (AWS), Google Cloud Platform (GCP), PyTorch, Artificial Intelligence (AI)

The most amazing...

...project I've built is an unsupervised model for an eCommerce startup that helped simulate complex nanomaterials and analyze large data sets in protein biology.


  • Mentor

    2021 - PRESENT
    Great Learning and Others
    • Mentored students and taught a full Python, AI, Machine Learning, and Business Analytics and Statistics course.
    • Trained graduate-level students in analyzing data and solving problems using Python.
    • Received a student rating of 4.7+ stars consistently for over 50 sessions.
    Technologies: Python, Artificial Intelligence (AI), Machine Learning, Mentorship, Business Analysis
  • Data Science Lead

    2020 - PRESENT
    • Built the app's symptoms feature, which allows the customers to relate user and site behavior as rage clicks, refreshing, broken buttons, and slow sessions, to be correlated with JavaScript and HTTP errors.
    • Developed a machine learning model that tells if an eCommerce bug is resolvable.
    • Created the first system in the industry that groups eCommerce errors and helps the customers understand the impact of a bug and make it easier for them to root cause and resolve a bug.
    Technologies: Rust, Python 3, Docker, Amazon Web Services (AWS), Google Cloud Platform (GCP), Elasticsearch, Big Data, Machine Learning, Unsupervised Learning


  • Resolvability of Bugs

    Built the first ML system from scratch at Noibu. The system predicted the likelihood that web bugs could be resolved, performed a thorough data analysis on the data that showed eCommerce bugs, and came up with an ML model that learned what kind of bugs were most likely to be resolved. Later, I collaborated with our design team and built a feature into our product that highlighted resolvable bugs that could be worked on easily. I created the model, tied the algorithm metrics to business KPIs, built the back-end in Rust, helped design the front-end and built a feedback system for continuous training and deployment.

  • Neural Networks for Automated Classification

    Created a neural network-based solution to automate a high-dimensional data set classification. Before my approach was developed, medical professionals labeled 15 column data set on blood cells in breast cancer patients. The data consisted of hundreds of thousands of samples with a lot of noise and required hours of manual labor to clean and label the data. In my approach, I trained an autoencoder that automatically classified the cells and removed them. The model was delivered as a software package that was easy to use and retrain. The process saved a lot of manual labor time and allowed the hospital to analyze data at a new scale that was not previously possible.

  • Failure Prediction of Devices

    Worked with a networking company to predict device failure in a given period. The objective was to predict the likelihood of a device failure. The company used this prediction to estimate the number of devices and which ones that would fail in the upcoming month to help prepare accordingly. The model I built was a deep learning model based on N-beats. The architecture was made for this particular purpose and performance well in predicting failures a few months into the future. The model was deployed on AWS, and I built the entire MLOps pipeline with support from other engineers.


  • Languages

    Python, SQL, Rust
  • Libraries/APIs

    Scikit-learn, XGBoost, TensorFlow, Keras
  • Paradigms

    Data Science, High-performance Computing
  • Platforms

    Google Cloud Platform (GCP), Amazon Web Services (AWS), Linux
  • Other

    Statistics, Data Analytics, Big Data, Machine Learning, Unsupervised Learning, Natural Language Processing (NLP), University Teaching, Mentorship & Coaching, Data Analysis, Neural Networks, Regression, Classification, Computer Science, Time Series, Machine Learning Operations (MLOps), Simulations, Computational Statistics, Predictive Modeling, Deep Learning, Artificial Intelligence (AI), Mentorship, Business Analysis
  • Storage



  • PhD in Physics
    2014 - 2018
    Technical University of Darmstadt - Darmstadt, Germany
  • Master's Degree in Computational Engineering
    2011 - 2013
    Univeristy at Buffalo - Buffalo, NY, USA

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