Egor Tkachenko

Lead ML Engineer

Moscow City, Russia3 yrs 2 mos experience

Most Likely To SwitchAI Enabled

Key Highlights

Proven expertise in quantitative research and market prediction.
Strong background in machine learning and data science applications.
Experience with high-frequency trading strategies and market-making.

Stackforce AI infers this person is a Quantitative Researcher specializing in Fintech and High-Frequency Trading.

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Skills

Core Skills

Machine LearningData ScienceQuantitative ResearchHigh-frequency Trading

Other Skills

C++CTR improvementprice binarizationtransformer-based ranking modelA/B testingmarket structurevolatilitytrend analysisATR-based regime selectionPythonDeep Learningbias-analysis pipelineMERA benchmarkmodel validationSQL

About

Quantitative Researcher with a background in Machine Learning, Deep Learning, Data Science, and Applied Mathematics. My quant experience comes from personal projects and competitions focused on market prediction, time-series modeling, high-frequency trading research, and optimization, including Wunder Fund Market Prediction, Jane Street Market Prediction, and the MSU Center for Mathematical Finance HFT accelerator. I am interested in alpha research, systematic trading, financial data science, predictive modeling, and building robust, testable signals from noisy data.

Experience

3 yrs 2 mos

Total Experience

1 yr

Average Tenure

1 yr 3 mos

Current Experience

Yandex

Machine Learning Engineer

Mar 2025 – Present · 1 yr 3 mos

Designed and deployed new marketplace features for the core product search ranking pipeline, improving CTR
by +0.15%.
Developed and productionized a new price binarization algorithm, improving CTR by +0.43%
over baseline.
Ran architectural experiments on a transformer-based ranking model, achieving a cumulative +0.35% Purchase rate gain.
Integrated features into high-load search infrastructure in C++ and supported A/B testing and analysis of search-
quality impact

C++CTR improvementprice binarizationtransformer-based ranking modelA/B testingMachine Learning+1

Independent

Quantitative Researcher

Feb 2025 – Mar 2025 · 1 mo · Moscow, Moscow City, Russia · Remote

Developed and executed a systematic market-making strategy across Binance altcoins using volatility, trend, and
ATR-based regime selection.
Dynamically adjusted quoting spreads (35–65 bps) based on intraday market conditions and short-term microstruc-
ture signals.
Implemented profit-taking logic at 15–20 bps with timed inventory risk reduction via scheduled market exits over
20-minute horizons.
Focused on execution efficiency, spread capture, and adaptive position management under volatile conditions.

market structurehigh-frequency tradingvolatilitytrend analysisATR-based regime selectionQuantitative Research+1

Sberbank-technology

Machine Learning Researcher

Aug 2023 – Feb 2025 · 1 yr 6 mos · Moscow, Moscow City, Russia · On-site

Supported regular evaluation of Russian-language LLMs by adapting the MERA benchmark for new strongest
Russian LLM GigaChat releases.
Built a bias-analysis pipeline based on ruBia and compared model behavior across subdomains and competitors.
Conducted full-cycle validation of retrieval and LLM models (GigaChat, Faiss+BERT+LoRA, BERT); im-
proved 6 production models, increasing precision/recall by 3–6%.
Redesigned aggregation for side-by-side annotations, improving expert agreement by 1.5x and increasing offline
metric stability.

PythonDeep Learningbias-analysis pipelineMERA benchmarkmodel validationMachine Learning+1

Yandex

Data Scientist

Jul 2021 – Dec 2021 · 5 mos · Moscow

Collected and prepared datasets from Zen logs using SQL and human annotations for ranking and moderation
models.
Built and deployed a TF-IDF + Logistic Regression classifier for anti-vaccination content used in the automatic
moderation pipeline.

SQLTF-IDFLogistic Regressiondata preparationData ScienceMachine Learning