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

You know who is trying to make you believe that AI will write 90% of the code by 2027?



It’s CEOs of these AI companies.

Not TOP researchers.

Not the ones who ship production every day.

It’s the ones who make billions from the air out of it.

And yet, people believe it.

Timur Bikmukhametov, PhD

Helping grow ML skills & careers in my ML Academy | World Top 3 ML LinkedIn Voice (Favikon)

Epoch8

Epoch8.co – is an agency for data analytics and machine learning projects.

Co-Founder, Head of Analytics

Эвотор

Business Development Manager

Mail.Ru Group

Product Manager

Readymag

Product Analyst

Enter.ru

Enter is one of the largest Russian multichannel retailers, having 80+ offline shops, e-commerce site, sales terminals and mobile applications.

Responsibilities:
Product Management:
— Product and UX development planning and roadmapping;
— Detailed new features description: product concept and UX wireframing;
— KPIs definition;
— Key projects supervision;
— Analytics (users behavior and business metrics).

Team Management:
— Managing the product team: 3 product managers, responsible for web site, mobile applications and CMS. My team coordinates work with ~20 web developers and content editors.

Head of Product Management

News360

News360 is a personalized news reader with more than 3 mln users across multiple platforms (iPhone, iPad, Andtoid Phone, Android Tablet, Windows 8, Web). News360 platform gathers news articles and blog posts around the web and, by learning what users read the most, brings them content that they’ll find interesting and important. 

News360 app was featured multiple times in both Apple appstore and Android market.

As a PM of News360 team (located in Moscow and San Francisco) I managed development of iPhone, iPad, Android Smartphone, Android Tablet, Windows 8 and Web versions of News360 app, including:
— working with business owners for business requirements gathering, analysis and specification;
— project task decomposition, scheduling and tracking;
— QA-management and release management;
— post-launch analytics and improvement: core KPI metrics definition, feature usage etc (using Google Analytics, Flurry Analytics, internal tools).

Achievements:
— 50+ releases of News360 apps across all platforms, including two super-major total redesign releases;
— introduced regular process of product research, development, testing and releases.

Project Manager

Undev.ru

I worked for technologically challenging TV and video broadcasting projects.

Completed projects:
— P2P video broadcasting platform (project manager);
— Information system for the St. Petersburg Economic Forum, 2011 (analyst).

1. P2P video broadcasting platform

Internal platform supporting video broadcasts organized by Digital October.

Technologies:
— TorrentCDN: content delivery system upon Bittorrent protocol (C++; Erlang);
— Server-side application for video capture and slicing (C++);
— Desktop applications for P2P video streaming (Windows, Mac, Linux).

2. Information system for the St. Petersburg Economic Forum, 2011
The information system allows operator to control 200 Forum video panels: schedule video broadcasts from conference rooms, display media content, real-time Twitter feeds etc.

Objectives:
— business requirements gathering, analysis and specification.

Project manager / Analyst

Kaspersky Lab

I was responsible for increasing online sales of Kaspersky products (Russia and EEMEA).

Objectives:
— Omniture SiteCatalyst implementation, working with Omniture to determine and improve typical user paths and conversion funnels; AB-testing of multiple page variations to improve conversion.
— Order process redesign, including both web site UI and business logic, resulting about 23% conversion lift.

Internet Solutions Manager / Web Analyst

Freelance

Web projects management from scratch to post launch analysis for big Russian media and FMCG brands (Il Patio, 1-2-3cafe, Media Arts communications group and some others).

Freelance Project Manager / Analyst

HeadHunter Group

UX designer / Product Manager

I'm a co-founder of Epoch8.co and Agima.ai, specialist agencies for machine learning and data analytics projects.

Why you might consider talking with us:
– Data analytics: if you have a startup and need a proper technical setup of all the analytical infrastructure. We help to set up ETL/ELT pipelines, gather data from all data sources, clean it up, describe it and prepare for reporting and self-service usage. Our focus is to do preparatory work to make it easier for the product teams to create reports quickly.
 
– Machine Learning: if you need Computer Vision functionality in your mobile app, for example recognizing details, photos, videos, people, signs and symbols, gestures, etc. We run ML models in real-time on the mobile device, so the user gets an experience of lightning-fast object detection.

Olga Tatarinova

"Most data engineers would be unemployed if pipelines stopped breaking" – saw that line on Reddit and stopped scrolling.

Because yeah – when data pipelines are on fire, everyone gets why you need data engineers. There’s a visible problem, someone jumps in to save the day, and it’s clear who’s adding value.

But then the team invests in architecture, fixes the root causes, and the fire alarm stops. And management starts asking "Why do we need such a big team?"
So, the team gets cut, process knowledge leaks out, and – surprise – the fires come back. 

So in most corporate cultures if you’re not running around fixing things, your work is invisible. You can try to make architecture, prevention, and quality visible, but business people will always say "Wait, we didn’t ask for that – why are we paying for all this?"
It sucks, but that’s how things often work.

And I think that’s exactly why our best projects at Epoch8 are the ones where we fully own data engineering for the client on a flat monthly fee, because businesses don’t want to keep a whole team of "on-call firefighters" in-house, they just want their pipelines to work. 

In many teams data engineering is like plumbing, you forget it’s there until something leaks.

We help founders and internal teams build computer vision, data, and AI products ❤️ eCommerce & retail, warehousing & logistics, LegalTech, HealthTech, startups

I have a real-life anecdote about following the hype. Around 10 years ago the hype was all about "big data".

Everything was "big data". Literally every boss would ask: "Are we gathering big data yet? Why not? When do we start?"

No one wanted to admit they had regular old boring data. 

One team I know finally got so sick of the hype, they made a separate schema in PostgreSQL and called it "Big Data".
In every C-level meeting they’d say, "According to our Big Data…" 
That dataset was tiny, but it didn’t matter: bosses were happy, everyone slept better. 

Fast forward. Maybe this is a leftover effect of that "big data" hype from 10 years ago, but the most common thing we see in clients’ data engineering stacks is that people still build "fake big data" systems for "small data" problems. 

And when we join the project, here's the checklist we use to untangle data problems. 

1. We start with SLAs. 
We ask: how fresh does the data need to be – minutes, hours, or daily? 
What’s the real cost if the data is late or wrong? 
Who is using this – dashboards, ML, finance? 
If it’s just daily reports, you don't need real-time streaming. 

2. We collect raw data from all data sources in one place. 
Just one DWH or even plain old Postgres, if that’s enough.  

3. We define one "source of truth". 
Curated, cleaned data goes here. All the data pipelines read from this (and not from each other). 

4. We separate "raw" and "modeled" data. 
And we don’t let anyone build reports directly off raw data.

5. We run batch processing by default and don't use streaming unless batch really isn’t enough . 
Streaming adds endless headaches: ordering, retries, idempotency, late events. Unless real-time is non-negotiable, we don’t take on the headache. 

6. Idempotency matters. 
Every job must be rerunnable. If we reprocess yesterday’s data, we shouldn’t create duplicates. 

7. Backfills are normal. 
We design so reprocessing a week or a month is no big deal. 

8. We implement just enough observability: row counts, freshness checks, job duration tracking.
We need to achieve "it broke and I noticed" state, and that's all. 

9. We always use orchestration tools – even in small data pipelines. 
Airflow, Dagster, Prefect are ok, but never just cron jobs. This way, you can retry runs, see dependencies, and know what failed. 

10. We optimize last. 
Most pipeline slowdowns are because of bad joins, bad partitioning or moving too much data around. Spark won’t save you if your basics suck. That’s it. 

---
We do a lot of this work at Epoch8: untangling overbuilt data stacks and helping people reclaim their cloud budget.

If you feel your pipeline is more complicated than your data, just write to me.
We can talk. Even if we don’t work together, at least we can help you sanity‑check your setup.

AI agents hate messy data almost as much as cats hate a full litter box.
So if your data looks like one… please don’t build an agent yet.

You probably don't need an AI agent right now

The Regrets of Data Engineering: what we’d do differently in our early projects (if we knew then what we know now)

I'm putting together some keynote stuff for the spring conference season. I'll share some takeaways from our experience along the way.

At Epoch8 we’re almost 9 years deep into heavy data engineering projects. Here are the common mistakes we used to make and try not to repeat now. 
Most of these came from chasing flexibility or speed way too early.

1️⃣ Trying to migrate business away from Excel
We used to force business users to use BI tools instead of Excel.

Didn’t work. You just get resistance, "shadow" Excel workflows, and, in the end, nobody trusts anything but their spreadsheet.
This battle is unwinnable.

Now we just accept Excel and provide good data exports from BI.

2️⃣ "Democratizing" DBT/Analytics Tools
Yeah, we used to let everyone create DBT models to speed things up and avoid DE bottlenecks.

As a result, we got thousands of overlapping or duplicate models, conflicting metrics, and costs out of control. 
Cleanup is an expensive political move.

Self-service analytics works, but only if you have a team responsible for data governance and curation. 

3️⃣ Over-abstracted Data Models or not using Data Models at all
We sinned both ways:
* Tried to build the "one universal data model" for all data sources
* Or just skipped modeling entirely

Consequence: if you over-abstract, new sources won’t fit, and every change is a pain. 
If you have no data model - reporting from raw data is super hard, reports break, nobody trusts anything.

Now we stick to Minimal Modeling (minimalmodeling.com) and use the same approach for all analysts and all projects. (At least we never end up with an SQL Server DB named MongoDB_final_v2_test_donotuse_prod)

4️⃣ Custom in-house tools over market solutions
We used to build our own tools even when open-source or vendor options existed.

Feels cool at first, super fast. Then, every shortcut bites you later.
When the original dev leaves, you’re stuck with a system nobody wants to touch.

Now we do our homework in terms of research and use standard tools unless there’s a dead-obvious reason not to.

5️⃣ New tools with uncertain futures
We used to jump on shiny, niche tools – and witnessed some of them lose support or just die.
Consequence: Can’t find maintainers, can’t hire, sometimes have to rewrite sooner than you think.

Now we pick mature, widely used (and open-source!) stuff for anything core.

---

What about you – do you have your own “never again” list in your projects?

taplio

The Battle

Timur Bikmukhametov

Olga Tatarinova

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taplio

The Battle

Timur Bikmukhametov

Olga Tatarinova

Followers

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Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Timur Bikmukhametov's LinkedIn Performance

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