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Yerkebulan Yerkinuly

@yrnkyrk

Classic ML engineer with Data engineering experience

Senior math student @ NU. Have experience in Data engineering and ML in classic ML.

Career

Experience: 2 years

Experience

Data Engineer Intern

EPAM Systems

Software engineering and IT services company.

Dec 2025 — May 2026

Built Spark and Databricks data pipelines on Azure, including Kafka streaming, ML workflows, and web scraping.

• Implemented a medallion architecture (bronze, silver, gold layers) in Databricks using Apache Spark for scalable data transformation and quality control. • Configured Azure services, integrating relational databases and real-time data stream flows into the pipeline infrastructure. • Built end-to-end machine learning pipelines in Databricks using MLlib, applying models to real-world datasets. • Developed a real-time data ingestion pipeline using Apache Kafka and visualized streaming metrics with Grafana.

Data Analysis Intern

Center for Digital Economy Development

Nov 2023 — Mar 2024

Scraped and processed location data, increasing database capacity by 300% and preparing 1,000+ shop records for analysis.

• Used Tableau to represent the correlation between schools, supermarkets, and alcohol locations. • Implemented Selenium to scrape 2GIS and increase database capacity by 300%. • Filtered 1,000+ raw shop entries and converted them into JSON files using Pandas and Seaborn.

Education

Bachelor, Mathematics

Nazarbayev University

2022 — 2027

Selected projects

Quantitative Equity Factor Strategy

Built a point-in-time factor-investing strategy on 300+ US equities from 1999–2020, engineering 100+ sector-neutral features with SEC filing-date controls to avoid look-ahead. Constructed long-only portfolios using top-40 selection and minimum-variance Markowitz optimization with Ledoit-Wolf shrinkage; reported 18.3% CAGR, 1.07 Sharpe, and +13.6% annual alpha vs S&P 500. Validated a walk-forwar...

Real-life ETL pipeline

Built Azure cloud infrastructure using Terraform and Docker and ingested over 20GB of raw data into Databricks. Designed a medallion architecture for structured transformation and quality control, and integrated Spark Streaming for continuous data flow from Azure into Databricks.

ML for Graphene Mechanical Property Prediction

Predicted four mechanical properties of functionalized graphene from structural encodings across 1,300 samples. Designed degree-based graph indices and persistent-homology-style Betti curve features. Gradient Boosted Regression achieved R2 > 0.87 on three targets, including R2 = 0.996 for Young’s modulus.

Voice Emotion Detection

Built a 2D-CNN model to predict voice actors’ emotions from WAV files. Experimented with pre-trained Wav2Vec2, increasing prediction accuracy by 15%, and applied data augmentation.

Achievements

Selected from 600 applicants for Beeline Bootcamp 2025

Beeline Bootcamp · 2025

NeHachaton finalist; won business case and ranked 1st among 30+ teams

NeHachaton

Won 2nd place among 20 teams

Datathon · 2024

Selected from 4,000+ candidates

Nfactorial Incubator

Completed with gold certificate and 96/100 points

Nfactorial Data Science, Tech-Orda program

Skills

PythonSQLPandasScikit-learnSeabornMatplotlibDockerK8sSparkDatabricksAirflowKafkaTerraformGrafanaSeleniumBeautifulSoupGitHubGitLabAzure