AIQ is the leading Industrial AI solution provider in the MENA region with a $1.4B valuation.

I am leading the development of an ML-based optimization solution for oil and gas production - RoboWell. The solution is deployed across 5 oil and gas fields with over 500 well counts.

𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:
✅ Identified 3 ML/MLOps sub-products and improved DS task delivery time by 20% by developing 
a roadmap, prioritizing tasks, and optimizing resource allocation for the solution scale-up phase. 
The sub-products include ML Models Monitoring Report, ML Proxy Model, and Data Quality Control.

✅ Saved $3M/year by developing end-to-end ML Proxy Modeling and Monitoring Pipelines that eliminated the cost of sub-contractors and reduced software license costs used for proxy model generation.

✅ Improved by 22% the ML solution MAPE error by designing model validation, data cleaning, and hyperparameter tuning strategies and supervising their implementation by the team.

Principal Data Scientist

Conundrum

Conundrum is an Industrial AI company that develops a SaaS platform for optimal control of metals and mining assets.

𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀:
🟢 Led a team of 7 data scientists in the development of machine learning modeling and optimization solutions.
🟢 Covered client communication, team management, problem formulation, model development, and solution deployment of complex ML / optimization solutions.
🟢 Data Science team hiring, growth, and development.
🟢 Support of the sales and business development from the AI-product strategy and pre-sale perspectives.

𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:
✅  Increased by $60M+ customer revenue by delivering 4 real-time end-to-end ML solutions that optimized ore processing and smelting plants. The solutions showed 2.5%+ increase of plants throughput and stabilized by 25%+ of the product variability.

✅  Identified potential $50M customer revenue improvement by completing 5 bottleneck analysis projects for Tier-1 metals and mining companies, resulting in new projects with them.

✅ Grew and structured a DS team from 5 to 20 data scientists who formed 4 internal teams responsible for specific solution directions.

✅ Supported closing of 3 multi-million contracts by providing technical support to the sales and business development teams.

✅ Reduced by 400%+ the time of development and deployment of ML applications by creating a machine learning pipeline template based on Kedro framework.

✅ Decreased from 15 to 3 days the data quality assessment time by creating a standard library that generated consistent data quality reports for new client data.

✅  Improved by 70% the DS team task completion rate within a sprint by introducing and adapting Scrum practices.

𝗨𝘀𝗲𝗱 𝘁𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀:
Git | GitLab | Kedro | Great Expectations | MLFlow | Scikit-Learn | XGBoost | CatBoost | PyTorch | Keras | Numpy | Pandas | SHAP | Plotly | Matpotlib | Seaborn | Scipy

Ctrl2GO Global

Ctrl2Go is one of the major AI solution providers for the railway and energy industries in the RCIS region.

𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀:
🟢 Led a team of 5 data scientists in the development of predictive monitoring machine learning models for various industrial equipment. 
🟢 Structured and optimized the company's business to enhance the efficiency of the data science team's contributions.

𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:
✅  Increased by 2.3% smelting plant production by supervising the solution development for optimal ore blending using MINLP formulation in Pyomo.

✅ Created a comprehensive model database/library for quicker deployment of the existing solutions in new projects.

✅ Formulated product development directions to the C-level management of the company based on the existing clients and partners and the data science team's expertise. It allowed the company management to prioritize development directions, understand risks, and estimate costs.

𝗨𝘀𝗲𝗱 𝘁𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀:
Git | GitLab | Kedro | Great Expectations | MLFlow | Scikit-Learn | XGBoost | CatBoost | PyTorch | Keras | Numpy | Pandas | SHAP | Plotly | Matpotlib | Seaborn | Scipy | Pyomo

Head of Data Science

Zyfra Company

Zyfra is one of the major Industrial AI solution providers in the RCIS region.

𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀:
🟢 Led a team of 3 data scientists in the development of ML solutions for problems in the petroleum industry including process modeling, optimization, and anomaly detection.
🟢 Covered Industrial AI-based product strategy formulation, management, development, and sales.

𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:
✅ Increased by 1.2% oil and gas production for 2 clients by leading the from-scratch development of the 
”AI Oil Optimization Assistant” solution. This is web-based ML software for accurate prediction and optimization of oil and gas production in real-time by using machine learning and advanced dynamic optimization algorithms.

✅ Increased by 5% the drilling speed and reduced by 50% the stuck pipe occurrence on a pilot test by developing a real-time ML solution to detect drilling pipe sticking events.

𝗨𝘀𝗲𝗱 𝘁𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀:
Git | GitLab | Kedro | Great Expectations | MLFlow | Scikit-Learn | XGBoost | CatBoost | PyTorch | Keras | Numpy | Pandas | SHAP | Plotly | Matpotlib | Seaborn | Scipy | Casadi

Senior Data Scientist

Norwegian University of Science and Technology (NTNU)

I was developing advanced industrial ML approaches for combining machine learning with process engineering physics for multiphase flow and reservoir modeling and equipment degradation monitoring.

𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:
✅ Reached TOP 6 most cited papers in Computers and Chemicals Engineering Journal (Q1) within
the last 3 years based on the Scopus citation system.
✅ Reached TOP 3 in the ranking of most downloaded papers for 2 publications in 2 Q1 journals.
✅ Reached over 300 citations within 3 years of active publishing.

𝗨𝘀𝗲𝗱 𝘁𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀:
Scikit-Learn | XGBoost | PyTorch | Keras | Numpy | Pandas | Plotly | Matpotlib | Seaborn | Linear Regression | Bayesian Neural Networks | Feed-Forward NN | Gradient Boosting | Random Forest

Researcher in Industrial Machine Learning

PSRE Co (NTP Truboprovod)

✅ Developed a model to predict severe slugging occurrence in pipeline networks and stability map representation.
✅ Developed a model to calculate transport of solids (flow regime and pressure drop) in single and multiphase steady-state flows for different flow patterns.
✅ Adopted industrial standards to calculate erosion rate in pipelines with single and multiphase flows.
✅ Developed a model of reciprocating compressors for equipment selection and performance evaluation purposes.

Multiphase Flow Models Developer

HOERBIGER

✅ Developed a calculation spreadsheet for the estimation of an oil production increase by wellhead compression technology.
✅ In a team of 2 application and 5 service engineers, completed 6 projects on supplying reciprocating compressor parts according to the customers’ needs.
✅ Assisted my colleague in a project of reciprocating compressor upgrade and revamp by developing a 3D compressor model in SolidWorks software.

Application Engineer

✅ Developed a dynamic model of a reciprocating compressor for CO2  compression in Modelica.

Visiting Researcher

Principal Data Scientist with over 8 years of experience in the full lifecycle of Machine Learning solutions, including strategy development, value discovery, code implementation, deployment, and monitoring. 

My expertise in ML and Optimization enables the creation of robust, accurate solutions, driving $3-10M in additional annual revenue per project.

As a Data Science Manager, I have 4 years of experience in leading, hiring, and managing data science teams. I am capable of organizing team work under harsh deadlines and complex project scopes. 

𝐈 𝐚𝐥𝐬𝐨 𝐞𝐧𝐣𝐨𝐲 𝐡𝐞𝐥𝐩𝐢𝐧𝐠 𝐨𝐭𝐡𝐞𝐫 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥𝐬 𝐠𝐫𝐨𝐰 𝐢𝐧 𝐭𝐡𝐞𝐢𝐫 𝐜𝐚𝐫𝐞𝐞𝐫 𝐛𝐲:
- Upgrading their CVs/LinkedIn
- Providing 1:1 Career Mentoring
- Sharing Free Practical ML Materials to support their technical growth

𝐌𝐲 𝐚𝐜𝐡𝐢𝐞𝐯𝐞𝐦𝐞𝐧𝐭𝐬 𝐨𝐯𝐞𝐫 𝐭𝐡𝐞 𝐩𝐚𝐬𝐭 𝐬𝐞𝐯𝐞𝐫𝐚𝐥 𝐲𝐞𝐚𝐫𝐬:

✅ Increased by $60M+ customer revenue by delivering 4 real-time end-to-end ML solutions that optimized Metals and Mining assets. During the projects, successfully managing the team of 7 Data Scientists. The solutions showed 2.5%+ increase of plants throughput and stabilized by 25%+ of the product variability.

✅ Identified potential $50M customer revenue improvement by completing 5 bottleneck analysis projects for Tier-1 metals and mining companies, resulting in new projects with them.

✅ Increased by 1.2% oil and gas production for 2 clients by leading the from-scratch development of the "AI Oil Optimization Assistant" solution. This is an ML software for accurate prediction and optimization of oil and gas production in real-time by using advanced ML and Optimization algorithms.

✅ Grew and structured a DS team from 5 to 20 data scientists who formed 4 internal teams responsible for specific solution directions.

► Tech Skills: Machine Learning | Data Science | AI | Data Analytics | Predictive Analytics | MLOps | Time Series | Anomaly Detection | Deployment  | Optimization | Git | Kedro | MLFlow | Scikit-Learn | XGBoost | Numpy | Pandas | SHAP |  Scipy | Docker

► Soft Skills: Team Management | Leadership | Empathy | Project Management | Presentation Skills | Problem Solving | Stakeholder Management

Timur Bikmukhametov, PhD

Cognite

Data Scientist

University of Tulsa

Research Assistant working in Tulsa University Horizontal Well Artificial Lift Projects
Core objectives and achievements: 
- Design new plunger lift experimental facility to study effect of deviations and characterize plungers
- Develop physics-based and machine learning models to design and optimize plunger lift systems
- Collaborated with MS student working on continuous plunger lift project
- Biannual presentation of project progress to consortia member companies
- Supervise undergraduate capstone projects

Research Assistant - PhD Student

Chevron

Interned at Chevron Energy Technology Company, Reservoir and Production Engineering Department
Core achievements: 
- Improved organizational capability of the team by completing transient simulations of gas lift and plunger lift using OLGA
- Demonstrated potential for 5-15 % of oil and gas production gain by installing plunger assisted gas lift
- Provided guidelines on optimizing plunger lift cycle time based on the performance of gas lift valves

Petroleum Engineering Intern

Research project was provided by Subsea Working Group
Core achievments:
- Designed and conducted slug flow experiments in floating pipes and lazy wave risers
- Generated experimental data for two-way coupled fluid structure simulator 
- Tested one- and two-way coupled flow-structure commercial simulators
- Worked in team with three laboratory members

Research Assistant - MS Student

ECCSEL, consortium aiming to provide access to reseach facilities focused on CCS technologies, provided transnational access to facilities of TNO
Core achievements:
- Developed documentation for transnational access 
- Analyzed different options for CO2 compression process in Dymola software
- Modeled dynamic behavior of reciprocating compressor in Modelica

Research Intern

IMS Group of companies

Engineering company specialized in designing and manufacturing of equipment for hydrocarbon production and transportation
Core achievments and responsibilities:
- Assisted in drawing of P&ID of Air and Nitrogen Generation Stations
- Conducted process calculations of gas pipelines and pig operations
- Developed program calculating gas blow operation of oil pipelines (used language: Delphi)
- Communicated with equipment suppliers

Gas Equipment Engineer (Part-time)

Academic Qualifications:

Ph.D in Petroleum Engineering at the University of Tulsa, with MS in Natural Gas Technology from the Norwegian University of Science and Technology and BS in Pipeline Engineering from Gubkin Russian State University of Oil and Gas.

Areas of Interest:

Petroleum Engineering, Multiphase Flow, Flow Assurance, Artificial Lift, Pipelines, Numerical Modeling, Data Analytics, Process Engineering

Programming Languages:

Python (numpy, scipy, matplotlib, sklearn, pandas, keras), MATLAB, FORTRAN

Software Packages:

OLGA, PIPEsim, Prosper, PVTsim, PROII

Goals:

Application of acquired knowledge in professional area, continuous learning and broadening of interests


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Timur Bikmukhametov, PhD

Anvar Akhiiartdinov

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