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

Honeywell

Industrial Software Engineering for Honeywell Connected Enterprise

Application Engineer II

Horizon Controls Group

•	Conducted kick-off meetings and collaborated with customers in Design Thinking Workshops to understand the requirement,   competency and setting short-term and long-term goals for Digital Twin development.
•	Study and review proposals and tenders for software solutions/services for process industries. 
•	  Configured data for an asset performance management solution for GSK Agileskid’s continuous biopharma processing plant
•	  Developed PI Vision training module and assisted in testing of PI Data Analysis training module for pharma processes for NIIMBL

System Engineer

Vojvodic Lab, University of Pennsylvania

•	Built preliminary pipelines for data extraction and pre-processing to obtain geometric and atomic properties of composite catalysts
•	Developed Python scripts for feature reduction (PCA) in order to identify critical descriptors for further in-depth analysis
•	Utilized SciKit Learn regression, gradient boosting, genetic programming and SISSO principles to develop python scripts in order to study the effects of various features on the adsorption of nitrogen and hydrogen in the catalytic synthesis of ammonia
•	Developed data visualizations to discuss the results of the analysis with the principal investigator and laboratory peers

Graduate Student Researcher

Honeywell Automation India Ltd

Connected Performance Services (Now Honeywell FORGE)

Software Applications :
Honeywell PHD, Aspen IP21, PI database, Honeywell SCOUT Express, UniSim Design, Uniformance Asset Sentinel, DataLake, Data Preprocessing Services

1. Collaborated with historian engineers on refinery sites for data acquisition and establishing continuous data transfer
2. Provided technical customer support  to clients for data flow connectivity between customer and Honeywell cloud environment
3. Collaborated to develop Process Optimization Advisor and Plant Reliability Advisor solutions with process design engineers (Honeywell UOP), software development and implementation team (Honeywell IT) and Data Visualization team (Honeywell IT)
4. Configured, developed and implemented Asset Performance Management modules for real-time process monitoring solutions 
5. Collaborated with data scientists for development, testing and troubleshooting of miscellanous services
6. Monitored system performance post patching and updates.
7. Manual testing for bugs or issues in software applications and creating workarounds, reporting issues and fixing them with the software development team

Operator Training Simulator / Dynamic Process Simulation : 

Refinery Units : Fluid Catalytic Cracking, Catalytic Cracking Reformer, Hydrocracking, Crude Distillation Unit, Vacuum Distillation Unit

1.  Identified project-scope, and marked up P&IDs for Operator Training Simulators
2.  Build dynamic UniSim Design models for different refinery processes
3.  Developed steady state models to validate Heat & Material Balances
4.  Integrated individual process area models into a single process plant
5.  Designed and developed a training module for design and simulation engineers from UOP and 
     Honeywell
4.  Performed start-up, shut down procedures and observed the plant behavior with an equipment 
     malfunction
5.  Monitored HMI plant graphics development for various refinery processes
6.  Performed simulation data analysis for Hydrocracking unit for Honeywell UOP Connected 
     Performance systems – a cloud- based service to monitor, predict and improve plant performance

Software Testing :
7. Performed manual testing for Honeywell UniSim R461 simulation software for bug identification
8. Assisted software development team in enhancing the software features and validation of software performance (Honeywell BRAVO Bronze award)

Application Engineer (Operator Training Simulation)

National Chemical Laboratory

Worked on 'Adsorption of Dyes on Treated and Raw Teakwood Saw-dust'- Research inclusive of the investigation of effect of size of saw-dust, pH of the dye-containing wastewater and concentration of saw-dust on the adsorption capacity.

Summer Research Intern

I am a chemical engineering professional with a background in dynamic process simulation, operator training simulation, and digital twins for refinery operations. I am looking forward to contribute in making the chemical plant operations safer and processes optimized with the help of digital methods.


Areas of Expertise:
Energy Engineering, Steady state and Dynamic Process Simulation, LIMS and DCS data acquisition, Historian Database Management, Asset Performance Management, Data Analysis for Chemical Engineering Applications

taplio

The Battle

Timur Bikmukhametov, PhD

Akshata Naik Nimbalkar, E.I.T.

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