Hossein is available for hire
Hire HosseinHossein Kalkhoran
Verified Expert in Engineering
Data Scientist and Developer
Location
Vancouver, Canada
Toptal Member Since
November 16, 2021
Hossein is a data scientist passionate about finding hidden patterns in large-scale data. He built an automatic vehicle tracking system using state-of-the-art deep learning algorithms for a large, multistory car park and designed an automated testing and submission system for the University of British Columbia. Combined with strong analytical, project management, and problem-solving skills, Hossein helps technology companies set up, build, test, and optimize their machine learning models.
Portfolio
Launcher Lab
Causal Inference, Python, Difference-in-differences (DID)
The Estée Lauder Companies
Google Cloud Platform (GCP), Statistical Methods, Hypothesis Testing, Python...
Mad Llama Studio
Pandas, Scikit-learn, PyTorch, Keras, NumPy, Tableau, Google Ads, Facebook Ads...
Experience
Availability
Part-time
Preferred Environment
Linux, Windows, RStudio, Google Cloud, Amazon Web Services (AWS), Python
The most amazing...
...project I've developed is an AI-powered chat system capable of classifying, filtering, and moderating text-based and image-based human interactions.
Work Experience
Senior Data Scientist
2023 - PRESENT
Launcher Lab
- Leveraged Difference-in-Differences (DiD) methodology to discern the causal impact of marketing initiatives, isolating effects from external factors and providing robust evidence for strategic decision-making.
- Developed and refined synthetic control models to create counterfactual scenarios, enabling precise measurement of marketing campaign performance against comparable non-exposed groups, resulting in optimized resource allocation.
- Worked closely with marketing and business strategy teams to translate complex causal inference findings into actionable insights, guiding campaign refinement and ensuring alignment with overarching business objectives.
Technologies: Causal Inference, Python, Difference-in-differences (DID)
Senior Marketing Data Scientist
2021 - PRESENT
The Estée Lauder Companies
- Developed and implemented an end-to-end MLOps workflow using GitHub Actions CI/CD to streamline the machine learning development and deployment process.
- Designed and created statistical experiments for estimating different marketing metrics using difference-in-difference analysis and Bayesian inference methods.
- Configured and designed automated build, test, and deployment pipelines for machine learning models, ensuring consistent and reproducible results.
- Implemented comprehensive A/B testing strategies on marketing data streams to optimize campaigns and drive business growth.
Technologies: Google Cloud Platform (GCP), Statistical Methods, Hypothesis Testing, Python, SQL, Docker, X Ray Engine, Polars, GitHub Actions, Apache Arrow, Data Science, Amazon Web Services (AWS), Linux, Quantitative Analysis, NumPy, CI/CD Pipelines, Machine Learning Operations (MLOps), Time Series Analysis, Time Series, A/B Testing, BigQuery
Machine Learning Engineer Lead
2018 - PRESENT
Mad Llama Studio
- Successfully built end-to-end machine learning pipelines, including data preprocessing, feature engineering, model training, and deployment on AWS.
- Optimized and fine-tuned machine learning algorithms and hyperparameters to achieve superior model performance and accuracy.
- Developed a data analysis dashboard capable of analyzing a real-time data stream and generating appropriate reports.
- Improved one of our client's eCommerce websites using predictive models. We optimized the website's performance metrics as 1) bounce rate: 21% decrease, 2) average session duration: 51% increase, and 3) pages and sessions: 18% increase.
- Managed to increase the sales volume of a client's online business by 2,000% in a 2-month project.
Technologies: Pandas, Scikit-learn, PyTorch, Keras, NumPy, Tableau, Google Ads, Facebook Ads, Digital Marketing, Data Science, Amazon Web Services (AWS), Linux, Quantitative Analysis, Docker, A/B Testing
Senior Data Scientist
2021 - 2021
Toptal Client
- Developed a risk assessment model associated with users' withdrawal and deposit limits on a crypto exchange platform.
- Designed the cloud architecture for deployment of the model into production.
- Created cloud architecture for monitoring the deployed model in production.
Technologies: Python, Jupyter, Cloud Architecture, Data Science, Amazon Web Services (AWS), CI/CD Pipelines, Linux, Quantitative Analysis, NumPy, Docker, A/B Testing, BigQuery
Senior Data Scientist
2020 - 2021
System Toose co.
- Led a team of four data scientists and three software developers.
- Designed and built an AI-powered chat system capable of classifying, filtering, and moderating text-based human interaction.
- Designed an AI-powered image recognition system capable of classifying sensitive or inappropriate images.
- Developed a time-series anomaly detection service that helps customers monitor various metrics. We took advantage of a simple yet strong, deep learning algorithm.
Technologies: Big Data, Python, GPT, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Image Processing, Machine Learning, Data Science, Amazon Web Services (AWS), Linux, Quantitative Analysis, NumPy, Docker, CI/CD Pipelines, A/B Testing, BigQuery
Data Scientist
2019 - 2020
System Toose co.
- Worked collaboratively in a team of deep learning researchers.
- Designed and implemented a big data pipeline using Apache Hadoop for analyzing over 350 GB of data.
- Built an automatic vehicle recognition and tracking system using state-of-the-art deep learning algorithms. The system was capable of detecting and tracking vehicles within a large multistory car park.
Technologies: Data Science, SQL, Modeling, Predictive Modeling, Data Visualization, Tableau, Big Data, Amazon Web Services (AWS), CI/CD Pipelines, Linux, Quantitative Analysis, NumPy, Docker, A/B Testing, BigQuery
Software Developer
2019 - 2020
The University of British Columbia
- Built a REST API back end for a learning analytics website.
- Designed and implemented an automatic testing and submission system for university students to increase the overall quality of computer science courses.
- Installed Linux and virtualized environments using Docker and AWS.
Technologies: Back-end, Django, Amazon EC2, Docker, Data Science, Amazon Web Services (AWS), Linux, Quantitative Analysis, NumPy, CI/CD Pipelines, Web Scraping, Data Scraping
Data Analyst
2015 - 2018
Motamed Cancer Institute
- Worked in the microbiome and bioinformatic lab as a research assistant.
- Designed and implemented a new mathematical model to predict the actual. drug release from a specific type of biomaterial.
- Designed and build data pipelines using Python and R for analyzing and optimizing experimental models.
Technologies: Python, Data Visualization, Mathematical Modeling, RStudio, Data Analysis, Data Science, Amazon Web Services (AWS), Linux, Quantitative Analysis, NumPy, Docker
Experience
Content Moderation System
A system for moderating text and image-based content on social media. Using natural language processing and image classification techniques, we designed predictive models capable of classifying, filtering, and moderating inappropriate text and image messages. In this project, I was the lead data scientist. My responsibilities included preparing our training dataset, designing the architecture of the models, leading a team of data scientists to train and optimize our models, and testing and evaluating our results.
Real-time Anomoly Detector
A system for unsupervised anomaly detection in multivariate time series data. This project was developed using a convolutional neural network and spectral residual to detect anomalies in time-sensitive data streams.
Traffic Monitoring System
A traffic monitoring system for a large multistory car parking company. I designed a multi-camera vehicle detection and tracking system. It was capable of vehicle re-identification in the client's multi-camera video surveillance system. The objective of the project was to track the activities of drivers on the premises.
Skills
Languages
Python, R, SQL
Libraries/APIs
Scikit-learn, PyTorch, NumPy, TensorFlow, Pandas, Keras
Tools
Tableau, BigQuery, Jupyter, Amazon SageMaker
Paradigms
Data Science
Platforms
Linux, Docker, Amazon Web Services (AWS), Google Cloud Platform (GCP), Windows, RStudio, Amazon EC2, Apache Arrow
Other
Machine Learning, Big Data, Data Analysis, Predictive Modeling, Quantitative Analysis, Natural Language Processing (NLP), Image Processing, Data Visualization, CI/CD Pipelines, A/B Testing, GPT, Generative Pre-trained Transformers (GPT), Google Ads, Facebook Ads, Digital Marketing, Time Series Analysis, Time Series, Back-end, Convolutional Neural Networks (CNN), Modeling, Mathematical Modeling, Cloud Architecture, Image Segmentation, Statistical Methods, Hypothesis Testing, Recommendation Systems, Classification Algorithms, X Ray Engine, Polars, GitHub Actions, Machine Learning Operations (MLOps), Web Scraping, Data Scraping, Causal Inference, Difference-in-differences (DID)
Storage
Google Cloud
Frameworks
Hadoop, Django, MXNet
Education
2019 - 2021
Master's Degree in Computer Science
University of British Columbia - Vancouver, Canada
2012 - 2016
Bachelor's Degree in Chemical Engineering
Iran University of Science and Technology - Tehran, Iran
Certifications
JANUARY 2022 - PRESENT
Object Detection with Amazon Sagemaker
Coursera
JANUARY 2022 - PRESENT
Image Classification with Amazon Sagemaker
Coursera
JANUARY 2022 - PRESENT
Building Recommendation System Using MXNET on AWS Sagemaker
Coursera
JANUARY 2022 - PRESENT
Semantic Segmentation with Amazon Sagemaker
Coursera
Collaboration That Works
How to Work with Toptal
Toptal matches you directly with global industry experts from our network in hours—not weeks or months.
1
Share your needs
Discuss your requirements and refine your scope in a call with a Toptal domain expert.
2
Choose your talent
Get a short list of expertly matched talent within 24 hours to review, interview, and choose from.
3
Start your risk-free talent trial
Work with your chosen talent on a trial basis for up to two weeks. Pay only if you decide to hire them.
Top talent is in high demand.
Start hiring