Emil A.
Data Scientist
With a solid theoretical background in engineering and mathematics, he possesses strong problem-solving skills and an analytical, well-organized approach. He swiftly adapts to new technologies, enabling him to make substantial contributions throughout the project's development and support life cycle.
He plays a crucial role in assisting business stakeholders by formulating effective solutions. Collaborating with business analysts, data architects, and data scientists, he leverages his expertise to create innovative models that identify new audience segments based on the model's output.
Main expertise
Python4 years
Data Science4 years
Machine Learning5 years
NumPy5 yearsExperience7
Senior Data Scientist
- Analyzed venue magic numbers to model the optimal number of partner stores per city, informing marketplace expansion strategies.
- Designed basket escalator analyses and free delivery threshold experiments to identify the optimal sales uplift point, directly influencing pricing strategy.
- Conducted large-scale A/B and CUPED experiments on marketing campaigns.
- Built data pipelines using Snowflake and Python and presented findings to senior stakeholders.
AWSPythonData Science
R (programming language)
Data Analytics
Agentic AI Developer
- Designed and developed an end-to-end multi-agent AI orchestration platform to automate marketing workflows and content generation.
- Developed specialized AI agents with tool-calling capabilities for content creation, media analysis, and workflow automation.
- Integrated custom-built AI models to replace external model and API calls, reducing latency, improving performance, and lowering operational costs.
MySQL
PostgreSQLPython
OpenAI API
LangChainDeep Learning Engineer
- Led the development of an agentic AI pipeline for automated stool image analysis, integrating image alignment, segmentation, object detection, and classification.
- Designed a hybrid computer vision workflow combining YOLO for bounding box detection, SAM (Segment Anything Model) for image segmentation, and advanced alignment methods (SuperGlue, DGC-Net, and GLUNet) to improve region-of-interest precision.
- Fine-tuned SAM using a custom COCO-annotated dataset and trained a U-Net CNN model for disease-relevant feature extraction and classification.
- Developed an end-to-end system supporting early disease detection in pets through robust and reproducible image analysis workflows.
PythonData Science
Google Cloud
PyTorch
Convolutional neural network
Lead Data Scientist
- Working with ELT and ETL pipelines of MySQL Server
- Transform R Studio codes into different tools
- Build Dashboards for the Technical support desk and Providers' KPI growth
MySQLPython
Microsoft Power BIR (programming language)
VPN
LookerData Science Specialist
- Ensured the integrity and reliability of ETL pipelines at Uptime and conducted analytical experiments to address challenges across various domains.
- Developed multiple Tableau dashboards to provide stakeholders with actionable, data-driven insights.
- Built machine learning models, including classification models to categorize schemas and handpump subsidy categories, and regression models to predict subsidy requirements and potential revenue.
- Developed a Streamlit application to streamline data analysis and enable interactive access to machine learning models.
- Contributed to a scientific paper documenting the findings and their implications.
- Improved decision-making and increased predictive accuracy by 20–25%, providing stakeholders with actionable insights to optimize subsidy allocation and revenue strategies.
AWSPython
SQLData Science
XGBoostMathematical Modelling Scientist
- Translating reaction needs into a testable hypothesis, proof writings, as well as prototyping pipelines that can be further developed and optimized by data engineers and used by data scientists for machine learning.
Data Science
OpenCVXGBoost
Scrapy
Computer Vision
Data Scientist
- Collecting and analyzing past and present data using relevant computer software;
- Identifying ways to improve operations and reduce costs based on data analysis;
- Managing a team of data scientists;
- Planning and overseeing data projects;
- Providing tools for making strategic decisions;
- Conducting independent research with problem definition, research plan, experimental design, algorithm development, code implementation, and benchmark comparison;
- Implementing cutting-edge predictive analytics solutions in finance, logistics, and sales;
- Analyzing research papers.
Engineering excellence
All Data Scientists who have applied to Proxify are scored from 0 to 300 on engineering excellence, one of the five parameters we evaluate. This score reflects engineering excellence only, based on interviews, take-home assignments, live coding sessions, and/or on-the-job performance reviews. The curve shows how all evaluated Data Scientists are distributed across that range, where our acceptance threshold for this parameter sits, and where Emil stands.
Score 229 · Top 2% of engineers
Certificates 1
Issued Jul 2026
Credential ID pke2rfmlsk
PythonLangChainIssued Jul 2026
Credential ID pke2rfmlsk
PythonLangChainEducation
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