Andrew is available for hire

Andrew Chauzov

Verified Expert in Engineering

Machine Learning Engineer and Developer

Location

Denpasar, Denpasar City, Bali, Indonesia

Toptal Member Since

April 2, 2020

Leveraging over a decade of expertise in data science, machine learning, and AI, Andrew has transformed data into actionable insights and strategic assets across various industries. This journey has led him to bring numerous projects from ideation to deployment, profoundly influencing product strategies and lineups.

Data Visualization Data Analysis Data Mining Data Modeling Analytics Statistics Data Analytics Statistical Data Analysis Machine Learning Natural Language Processing (NLP)Artificial Intelligence (AI)Prompt Engineering SQL MySQL PyCharm

Portfolio

CRED Investments UK Ltd

Data Visualization, API Development, Deep Learning...

Independent Consulting Services

Predictive Modeling, Machine Learning, Data Visualization, Deep Learning...

Association 'Non-Profit Market Council'

Data Visualization, Predictive Modeling, Statistical Analysis, Data Analysis...

Experience

Data Science - 12 years Predictive Modeling - 12 years Machine Learning - 9 years Natural Language Processing (NLP) - 7 years Python - 7 years Artificial Intelligence (AI) - 5 years Large Language Models (LLMs) - 3 years Generative AI - 1 year

Availability

Part-time

Preferred Environment

PyCharm, GitHub, Google Cloud Platform (GCP), Amazon Web Services (AWS)

The most amazing...

...thing I've done was develop a player-team fit algorithm using football data that enhances talent scouting and reshapes team strategies.

Work Experience

Data Scientist | ML Engineer | Data-Driven Analytics

2020 - 2023

CRED Investments UK Ltd

Brought over 25 projects from ideation to deployment, including several foundation models, delivering data-driven insights and diversifying the company’s product lineup.
Developed LLM-based multimodal models for data enrichment and standardization, restoring 90% of missing data (around 450 million points), enhancing data quality, and directly boosting revenue growth in client projects.
Collaborated on 10+ sports analytics models that professional scouts and clubs adopted. These models optimized scouting processes and costs and are featured in mobile apps and dashboards.
Initiated data quality initiatives, including a game statistics pipeline that improved the precision of key predictive models, impacting the accuracy of player performance assessments.
Built statistical models that introduced matching/recommendations between 100,000+ businesses and 400 million consumers, directly reducing customer attraction costs.
Established a dual-layer regression model for football market value forecasts with <10% error, improving strategic decision-making for industry experts.
Deployed an athlete retirement prediction model with a one-season margin of error, managing data irregularities and guiding strategic investments.
Engineered a salary prediction pipeline with less than 10% error, providing insights that deepened client understanding of consumer demographics.
Innovated algorithms for customer data refinement, enhancing segmentation and impacting ad campaigns, reducing marketing expenditures.
Leveraged generative AI to analyze and process 10 million social media profiles, adding depth to our datasets.

Technologies: Data Visualization, API Development, Deep Learning, Natural Language Processing (NLP), Google Cloud Platform (GCP), Python, Data Analysis, Statistical Analysis, Machine Learning, Predictive Modeling, SQL, TensorFlow, PyTorch, NumPy, Data Mining, Docker, Data Science, Communication, Linux, Artificial Intelligence (AI), Software Development, Large Language Models (LLMs), Big Data, Programming, Cloud Computing, Optimization, Teamwork, Analytical Thinking, Data Analytics, Data Modeling, Forecasting, Presentations, PL/SQL, Algorithms, English, Generative AI, Analytics, Databases, Prompt Engineering, ChatGPT, Git, Statistics

DS and ML Consultant | Algorithm Design & NLP

2017 - 2022

Independent Consulting Services

Collaborated with startups and consultancies to develop ML solutions (predictive analytics, forecasting, and NLP), achieving cost savings and revenue growth. The role required teamwork, self-management, and an understanding of client needs.
Created an employee churn detection model with HR, providing actionable insights praised by stakeholders. Boosted retention and identified 10+ at-risk employees.
Developed a PDF processing algorithm that extracts structured data from diverse financial documents dating back 10 years (totaling over 1,000 papers); this achieved an accuracy rate of over 95%, enabling custom search functionalities.
Innovated a speech anomaly detection algorithm with 70-95% accuracy across more than 40 defect types. This solution, resulting from collaborative R&D efforts, implemented a core update to a healthcare mobile app.
Devised a horse race betting system offering real-time, low-latency betting suggestions. This method doubled the performance of the previous system and resulted in an approximate 2% increase in revenue.
Created a sales volume clustering algorithm, which led to a 15% improvement in sales planning and effectiveness.

Technologies: Predictive Modeling, Machine Learning, Data Visualization, Deep Learning, Python, TensorFlow, API Development, Data Science, Data Analysis, Statistical Analysis, Natural Language Processing (NLP), SQL, NumPy, Data Mining, Communication, Docker, Linux, Software Development, Programming, Project Management, Cloud Computing, Amazon Web Services (AWS), Optimization, PyTorch, Computer Vision, Teamwork, Mathematics, Analytical Thinking, Data Analytics, Data Modeling, MySQL, R, Forecasting, Presentations, Google Cloud Platform (GCP), Algorithms, English, Analytics, Databases, Big Data, Artificial Intelligence (AI), Git, Statistics

Middle/Senior Data Analyst | Modeling Impact & Revenue Growth

2012 - 2016

Association 'Non-Profit Market Council'

Played a key role in developing data-driven strategies and managing data processes and analytics. As senior analyst, led projects enhancing decision-making, yielding 2.5-10% revenue increases in the following year.
Mentored and led three junior analysts, fostering a learning and professional growth culture. The guidance facilitated skill enhancement and resulted in two promotions within the year, demonstrating a commitment to team development and leadership.
Revolutionized operational efficiency within the team, reducing task completion time from four days to eight hours through improving automation scripts, impacting the department's proficiency in delivering quick and accurate reports and insights.
Applied time series analysis and data science techniques for anomaly detection, identifying around 50 critical periods annually. This led to a 5-25% reduction in forecast error rates for power price/volume predictions in targeted regions.
Introduced a data enrichment algorithm, aggregating daily data into weekly and monthly summaries; this innovation improved analysis accuracy during volatile periods, contributing to more reliable forecasting models.
Promoted to senior analyst in 2014 for exceptional predictive modeling expertise and productivity enhancements, having developed over 10 models that influenced strategic decisions and operational efficiency.
Pitched and received approval for implementing five predictive models in over 20 stakeholder meetings by communicating their technical and business impact.

Technologies: Data Visualization, Predictive Modeling, Statistical Analysis, Data Analysis, SQL, Data Science, Machine Learning, Data Mining, Communication, Programming, Project Management, Optimization, Teamwork, Mathematics, C++, Analytical Thinking, Data Analytics, Data Modeling, MySQL, R, Forecasting, Presentations, Algorithms, Analytics, Databases, Statistics

Junior Data Analyst | Data Processing & Analytical Modeling

2011 - 2012

BrandScience

Facilitated execution of data-centric strategies, focusing on data processing and analytical modeling and driving insights influencing strategic decisions.
Developed and implemented SQL and VBA-based aggregation logic, incorporating correlation analysis to expand data by 2.5x and enable multiple data sources for modeling, thereby enhancing reliability and supporting more informed decision-making.
Streamlined media data collection (CATI/CAWI) and processing by introducing automated scripts, increasing departmental task efficiency by 300%, and reducing algorithm execution time from two hours to 30 minutes while making it fully automatic.
Initiated cluster analysis to estimate early-stage campaign efficiency, enabling more strategic budget pre-allocation. This approach improved budget allocation effectiveness by an average of 20% across over 10 campaigns.
Enhanced ROI models by integrating a VBA-based anomaly detection function, stabilizing predictions at the early forecasting stages, reducing expenditures by 50%, and boosting client marketing budget efficiency.
Collaborated in refining the ROI prediction regression model using Excel/VBA, boosting campaign efficiency by 10% (improving brand knowledge from approximately 80% to 90%).

Technologies: Data Visualization, Predictive Modeling, Statistical Analysis, SQL, Data Analysis, Data Science, Data Mining, Communication, Statistical Data Analysis, Optimization, Teamwork, Mathematics, Analytical Thinking, Data Analytics, Data Modeling, MySQL, Forecasting, Presentations, Algorithms, English, Analytics, Databases, Statistics

Experience

AutoML: Unsupervised Model Training with Optuna & SHAP Feature Selection

https://github.com/avchauzov/ml_training_optuna_shap

OBJECTIVE:
Aimed to build an AutoML platform, leveraging seasoned expertise and the best methodologies for optimal performance.

APPROACH
Focused on refining hyperparameter optimization for greater efficiency, innovating in feature selection for more profound results, and ensuring development integrity through rigorous testing, making the system robust and user-friendly.

RESULT
The platform now effectively supports essential model types like linear, gradient boosting, and Naive Bayes, with ongoing enhancements to broaden its modeling capabilities and application scope.

Web Scraping: Efficient Data Collection Using Selenium and Headless Chrome

https://github.com/avchauzov/teamform_web_scraping

OBJECTIVE
To create a web scraping tool capable of gathering league ranking data from TeamForm, utilizing a headless Chrome browser for efficient data collection.

APPROACH
Deployed Selenium for automated web navigation and scraping, with added functionality to manage data load for memory efficiency. The design also allows for future expansion to collect more detailed 'Club' and 'National' data.

RESULT
Successfully extracted league ranking data, offering valuable insights into team standings and performance, and set the groundwork for expanded data retrieval capabilities.

Algorithm: DTW-Based Hierarchical Clustering for FMCG Sales Time Series Analysis

https://github.com/avchauzov/time_series_clustering

OBJECTIVE
To unlock insights within consumer goods sales data through detailed analysis and clustering to highlight patterns and trends.

APPROACH
Conducted thorough time series analysis, including data cleaning for quality and employing Dynamic Time Warping (DTW) to pinpoint similarities, alongside developing a NumPy-based clustering algorithm for efficient data aggregation.

RESULT
Successfully clustered over 10,000 time series data points, revealing meaningful patterns and trends, significantly enhancing data understanding and strategic planning capabilities.

Skills

Languages

Python, SQL, R, C++

Libraries/APIs

NumPy, API Development, TensorFlow, PyTorch

Tools

PyCharm, Git, ChatGPT, GitHub

Paradigms

Data Science

Storage

MySQL, PL/SQL, Databases

Other

Data Visualization, Machine Learning, Predictive Modeling, Natural Language Processing (NLP), Data Analysis, Data Mining, Communication, Artificial Intelligence (AI), Data Modeling, Optimization, Analytical Thinking, Analytics, Statistics, Prompt Engineering, Cloud Computing, Teamwork, Presentations, Forecasting, Data Analytics, Statistical Data Analysis, Mathematics, Statistical Analysis, Programming, Deep Learning, Software Development, Large Language Models (LLMs), Big Data, Generative AI, English, Algorithms, Computer Vision, Computer Science

Platforms

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

Industry Expertise

Project Management

Education

2004 - 2011

Master's Degree in Applied Mathematics and Computer Science

Peoples' Friendship University of Russia - Moscow, Russia

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