Ildar A.N.

Hello! My name is Ildar Nakiullovich Abdulin. I am Applied Mathematician / Data Scientist in physics-based and data-driven predictive modelling with 5+ years experience, including 1.5 years in energy corporation and 3.5 years in R&D for industrial problems.
Experinced in Statistics/ML/Math Model Prototyping (incl. from scratch), searching-for bottlenecks in modeling pipelines, Data Mining.
Proficient in Python 3, Matlab/Maple, C/C++, VBA.
Author of three peer-reviewed publications on the mathematical modelling.

LinkedIn: linkedin.com/in/ildar-an
Location/Work Permit: Netherlands
Email: ildar.nakiullovich@outlook.com (open for new projects)

📝 Main Publications

1. Production forecast method based on statistical analysis of a small sample of production data for an unconventional formation, Oil Industry Journal (Scopus), 2021.
2. Identification of fractal properties and parameters upscaling of layered heterogeneous medium, Oil Industry Journal (Scopus), 2020.
3. Numerical investigation of radial steady-state fluid flow model with Riesz potential, American Institute of Physics Conference Series (Scopus), 2020.
Full list: Google Scholar.

📈 Experience

Data Scientist / Mathematician – Freelance
2015 – now
Data Mining projects, EDA, consultation, tutoring in advanced math (students).

Applied Mathematician / Data Scientist – Gazprom Neft Oil Company (About)
Russian energy corporation. Wiki Page. The Technology Partnerships LLC (Bazhen Technology Centre) department, based in Moscow, specializes in shale oil production modeling.
About Department,
| Project-based employment
03/2020 – 09/2020, 08/2021 – 01/2022, 08/2022 – 01/2023 (1.5 years total)
Worked on 3 projects focused on analysis and development of predictive modeling modules for oil production.
Responsibilities:
• Implemented modeling solutions using Python 3 and VBA.
• Research on predictive modeling techniques in oil productions.
• Development of technical requirement and documentation.
Publications: [1].

Research Engineer (Math Modelling) – GAMMETT Research Laboratory, USATU (About)
Based at Ufa State Aviation Technical University (Russia), collaborated with the research institute RN-BashNIPIneft on oil production modeling research.
Website.
| Fulltime
03/2017 – 11/2020 (3 years 8 months)
Problem detection in oil production modeling with modern mathematics (fractals, fractional derivatives) .
Responsibilities:
• Prototyped modeling solutions using Matlab/Maple and Python 3.
• Research on modeling techniques to account for new phenomena in oil production.
• Published academic papers in mathematical modeling and research documentation.
Publications: [2, 3, etc.].

🎓 Education and Certifications

MSc of Applied Mathematics and Computer Science, Ufa State Aviation Technical University (USATU), Russia.
BSc of Applied Mathematics and Computer Science, USATU, Russia.
2021, Online Course, Machine Learning, Andrew Ng, Coursera, [Coursera Certificate].
Topics: Logistic Regression, Regularization, Neural Networks, Support Vector Machines, Dimensionality Reduction, Anomaly Detection, Recommender Systems. Programming Language: Matlab.
2020, Online Course, Data Analysis Based on Machine Learning, BashSU, Ufa, Russia.
Topics: Decision Trees, Random Forest, Regression Analysis, Gauss-Markov Theorem, Naive Bayes, Model Ensembles, Dataset Balancing. Programming Language: R.
2024, HackerRank, Python Test Certificate.

💼 Projects

▶ 1. Freelance | Data Mimig of Real Estate Segment: Parsing and searching-for undervalued commercial properties. | Python 3 | 05/2023 - 07/2023 • Objective: Identify systematically undervalued commercial properties in high-potential Moscow districts
• Implementation: Built CIAN.ru web scraper (BeautifulSoup/CloudScraper) + XGBoost valuation model with expert-defined feature engineering
• Outcome: Delivered 3 expert-validated investment opportunities within MVP period, reducing noise by 40% through NLP filtering [GitHub: Parser Architecture] ▶ 2. Gazprom Neft | Model Development for Oil Well Production Forecasting: Nonlinear Differential Equation, Numeric Methods. | Python 3 | 08/2022 – 01/2023 • Objective: Develop open-source alternative to commercial simulators for production forecasting under reservoir engineering constraints
• Implementation: Designed finite element solver (FiPy) implementing pressure-dependent permeability modeling (2D Diffusion Equation), benchmarked against tNavigator
• Outcome: Achieved 95-99% parity with industry-standard simulator results while eliminating arbitrary parameter tuning [GitHub: Solver Implementation] ▶ 3. Gazprom Neft | Excel-to-VBA Forecasting Model Transformation. | VBA | 08/2021 – 01/2022 • Objective: Modernize legacy spreadsheet-based forecasting model (600+ formulas across 7 sheets)
• Implementation: Developed automated formula parser converting Excel logic to modular VBA with unified GUI interface
• Outcome: Eliminated manual calculation errors and improved operational efficiency through single-interface solution [GitHub: Conversion Module] ▶ 4. Gazprom Neft | Data Mining of Small Group of Oil Wells: Regression Model Development and Monte Carlo Sampling. | Python 3 | 03/2020 – 09/2020 | [1] – Publication • Objective: Quantify key production influencers and develop probabilistic forecasting framework
• Implementation: Multivariate regression analysis + Monte Carlo simulation
• Outcome: Identified 5 statistically significant production drivers with SHAP value interpretation ▶ 5. Research lab. GAMMETT | Fractal Model Architecture for Oil Production: Inverse Problem, Testing on Real Data. | Maple | 2020 | [2, 4] – Publications • Objective: Incorporate fractal properties of the medium into a mathematical model for oil pressure.
• Implementation: Designed a new model in collaboration with a Physicist Scientist. Tested using real-world data.
• Outcome: The use of the model can improve the accuracy of predicted values by up to 20%. ▶ 6. Research lab. GAMMETT | New Model Numeric Method: Singular PDE. | Maple/Python 3 | 2019 | [3] – Publication • Objective: Develop stable numerical scheme for integro-differential equation
• Implementation: Created adaptive mesh refinement algorithm handling solution singularities
• Outcome: Published convergence proof and field validation results ▶ 7. Research lab. GAMMETT | Fractal Derivatives in Oil Production Modeling: Monte Carlo algorithm prototyping, Differential Equations. | Maple / Python 3 | 2018 | [5, 6] – Publications • Objective: Develop and compare new modeling approach (fractal derivative) for physical model with differential equations (oil production)
• Implementation: Prototyped a Monte Carlo algorithm article arxiv.org/abs/0906.0676. Developed a model with fractal derivatives in collaboration with two Research Scientists.
• Outcome: Conducted comparative analysis with a simpler model. [EN Description]

🛠️ Skills

Concepts: Math Model/ML/Statistics Prototyping, Data Minig, Monte Carlo Sampling.
Programming: Python 3, Maple/Matlab, C/C++, VBA, SQL.
Languages: English fluent, Russian native.