Part designation:

Sergey
Kasatov

Focus: Launch & Quality Engineering · Supplier Quality · Cologne, DE

Seventeen years at the line, and now the data toolkit to go with it.

Body-in-white and closures development, launch management and supplier quality on Ford, General Motors, Opel and Stellantis programs. Retrained in Python, SQL, Tableau and machine learning in 2025-2026. Available now for launch, quality, supplier-quality and manufacturing engineering roles in Germany, and for data roles inside manufacturing.

Experience record Get in touch

LinkedIn Email AI & data profile

Released for hireavailable now · DE

Fig. 1 · SPC run chart wk 1–24

Issue time-to-close: ~4 wk → ~3 wk · Stellantis program

TitleSergey Kasatov · Launch & Quality

LocationCologne, DE

Projection

RevG · 2026

StatusReleased for hire · available now

17 years automotive engineering 7 vehicle launches 20+ supplier audits €250k largest cost saving 8D · SPC · PPAP/APQP Ford · GM · Opel · Stellantis Cologne, Germany

17yrs

automotive engineering: development, quality, launch

vehicle launches supported with plants and suppliers

20+

supplier audits · PPAP/APQP for 35 parts

€250k

largest documented cost saving per initiative

Section A-A · Why this profile

The line, the supplier and the KPI pack

Every side of the OEM-plant-supplier triangle, and the reporting that moved the projects.

Launch management

Supported 7 vehicle launches with 3 plants and 5 suppliers at Ford, leading a team of 3 engineers. Led development-to-launch integration for ~100 body-in-white parts and coordinated ~150 engineering changes through ramp-up at Stellantis.

Supplier quality and audits

Audited 20+ suppliers and ran PPAP/APQP for 35 parts at SOLLERS, closing 8 critical issues before SOP. 8D, SPC, PPAP/APQP and supplier audits are working methods from those years, not slide titles.

KPI reporting and root cause

A weekly KPI / quality trend pack cut issue time-to-close from ~4 to ~3 weeks and launch-phase rework by ~25% at Stellantis. Today I build the same packs and root-cause analyses with SQL, Python, Excel and Tableau.

Revision history · Experience

17 years across the vehicle lifecycle

Read it like a drawing's revision block, oldest first. Detail views of every revision are on the About page.

Rev	Date	Description of change	Where
A	2007–2008	Process engineering: CNC programs, machining lines, aerospace documentation	ElaS & KMPO, Russia
B	2008–2010	Senior Supplier Quality Engineer: 20+ supplier audits, PPAP/APQP for 35 parts	SOLLERS · FIAT & ISUZU
C	2011–2012	Engineering internships: prototype readiness, UHSS repair methods, simulations	Ford & Continental, DE
D	2013–2014	Product Engineer: localized 25 BIW parts, €75k savings	General Motors, RU
E	2014–2025	Automotive Consultant: development, launch, quality on Ford, Opel, Stellantis programs	SEGULA Technologies, DE
F	2025–2026	Retrained into data analytics: Python, SQL, Tableau, ML	Masterschool · Cologne

Rev. E · Detail view Apr 2014 – Feb 2025

SEGULA Technologies · Automotive Consultant

Cologne, Germany · long-term assignments at Stellantis, Opel, and Ford

Project Engineer, BIW & Door Components · Stellantis, Rüsselsheim

Jul 2022 – Feb 2025

Led development-to-launch integration for ~100 body-in-white parts and coordinated ~150 engineering changes through ramp-up. A weekly KPI / quality trend pack cut issue time-to-close from ~4 to ~3 weeks and launch-phase rework by ~25%, delivering a €100k cost saving.

Launch Quality Specialist, TSQ · Opel Plant Eisenach

Oct 2020 – Jun 2022

Secured launch quality for Opel Grandland X Facelift serial production: tracked vehicles through assembly, resolved line-side issues, and documented corrective and preventive measures.

Manufacturing Engineer, Launch & Logistics · Eisenach, Rüsselsheim, Trnava

Jul 2019 – Sep 2020

Material-flow analysis for a PHEV introduction, Supermarket and E-Kanban optimization, assembly concepts for new model integration, and robot adaptation for the Peugeot 208 BEV line.

Group Leader, Body Exterior & Interior · Ford, Cologne

Apr 2014 – Jun 2019

Supported 7 vehicle launches with 3 plants and 5 suppliers, leading a team of 3 engineers. Weekly KPI pack for 6 stakeholders tracked SOP readiness of 70+ parts. Standardization and localization initiatives delivered €250k in cost savings.

KPI reportinglaunch managementengineering changeroot cause analysis

All detail views, education and certificates

Technical specification · Skills

Two toolboxes, one profile

The quality discipline of an automotive engineer, and the data stack to work the numbers directly.

Quality & Manufacturing Spec 3/4

Root cause analysis · 8D SPC PPAP / APQP Supplier quality & audits Launch / ramp-up management Engineering change management Lean · continuous improvement

Data toolkit Spec 1/4

SQL Python · pandas Tableau Excel / Power Query Statistics & EDA Data cleaning KPI design & dashboards Data storytelling

Parts list · Manufacturing-relevant

Data work in plant language

Three of thirteen portfolio projects, chosen for a plant or quality audience. Every metric is reproducible from the linked repository.

Pos. 04 · Apr–May 2026

Horizontal bar chart of the 20 most important features by mean absolute SHAP value; Brake_Condition dominates at about 2.8, every other feature is below 0.2 — **Fig. 04**Mean |SHAP| feature importance, XGBoost · repository figure

Predictive Maintenance: Fleet Risk & Cost

Three business questions answered on a 92,000-row logistics fleet dataset: which vehicles need maintenance (classification), what it will cost (regression), and how to segment the fleet by risk (clustering). SHAP analysis identified brake condition as the dominant failure predictor, exactly what 17 years in vehicle engineering would suggest.

PR-AUC 0.902 R² 0.885 rows 92k

PythonXGBoostSHAPSQLClustering

Case study PS-02 Code & notebooks

Pos. 12 · Jul 2026

PowerShell window listing seven report files, each with verdict PASS, the number of gap markers found and the sections they were marked in, ending with 7 of 7 passed — **Fig. 12**Marker checker run, 7 of 7 passed · repository screenshot

IATF Quality Report Prompt

Can a language model be stopped from filling a gap in a controlled record? A three-zone prompt drafts German ISO 9001 / IATF 16949 quality reports and is forbidden to derive any figure it was not given: in a QMS record a derived figure is an audit finding even when the arithmetic is right. A negative-control test withheld one figure and left both operands in as bait. No run computed it; the gap marker appeared in both required places in only 5 of 7 runs, and that weaker number is published as found, prompt deliberately unfixed.

no invented figures 7 of 7 runs gap marked twice 5 of 7 own defects published 3

Claude Opus 5Prompt engineeringTest designPython

Prompt, test & transcripts

Pos. 06 · Feb 2026 Live

Retail KPI Dashboard

Interactive Tableau dashboard tracking sales, profit, discount, and orders with drill-downs by region, product category, and time. LOD expressions and dashboard actions give non-technical users self-service analysis, the same logic as a quality trend board in a plant.

rows ~10k months 48 states 49

TableauLODDashboardActions

Open on Tableau Public

Full parts list: all 13 projects

Need someone who knows the line and the numbers?

Available now for launch, quality, supplier-quality and manufacturing engineering roles in Germany: on-site, hybrid or remote. Also open to data and automation roles inside manufacturing. German B2, English C1, Russian native.

sergey@kasatov.de Connect on LinkedIn