Hiroshi Kaneko - Senior Data Scientist

Q: Where is Hiroshi based?

Hiroshi is based in Warsaw, Poland .

Q: What languages does Hiroshi speak?

Hiroshi speaks the following languages: Japanese (Native), German (Advanced), English (Advanced) .

Q: How many years of experience does Hiroshi have?

Hiroshi has at least 11 years of experience. During this time, Hiroshi has worked in at least 4 different roles and for 3 different companies . The average length of individual experience is 3 years and 10 months . Note that Hiroshi may not have shared all experience and actually has more experience.

Q: What is Hiroshi's latest experience?

Hiroshi's most recent position is Senior Data Scientist at Siemens .

Q: What companies has Hiroshi worked for in recent years?

In recent years, Hiroshi has worked for Siemens , EPAM Systems , and Fujitsu .

Q: Which industries is Hiroshi most experienced in?

Hiroshi is most experienced in industries like Information Technology (IT) , Banking and Finance , and Manufacturing .

Q: Which business areas is Hiroshi most experienced in?

Hiroshi is most experienced in business areas like Information Technology (IT) , Quality Assurance (QA) , and Product Development . Hiroshi also has some experience in Business Intelligence .

Q: Which industries has Hiroshi worked in recently?

Hiroshi has recently worked in industries like Banking and Finance , Information Technology (IT) , and Manufacturing .

Q: Which business areas has Hiroshi worked in recently?

Hiroshi has recently worked in business areas like Information Technology (IT) , Quality Assurance (QA) , and Business Intelligence .

Recommended expert

Warsaw, Poland

Experience

Oct 2024 - Sep 2025

1 year

Munich, Germany

Senior Data Scientist

Siemens

Addressed inconsistent annotation quality across distributed teams by implementing a tiered review system with structured feedback templates, reducing rework by 35% and improving inter-annotator agreement to 0.85 Cohen's kappa.
Solved factual accuracy degradation in AI-generated technical documentation by developing a multi-stage verification workflow combining automated fact-checking with expert human review, achieving 98% accuracy on manufacturing specification documents.
Eliminated sensitive content leakage in training data by designing PII detection filters and establishing redaction protocols for IoT sensor data and maintenance logs, ensuring compliance with GDPR and internal security policies.
Streamlined annotation team onboarding by creating comprehensive training workshops and mentorship programs, reducing ramp-up time from 6 weeks to 3 weeks while maintaining quality standards.
Resolved guideline interpretation inconsistencies by developing detailed annotation protocols with concrete examples and edge cases, decreasing clarification requests by 60% across multilingual annotation projects.
Fixed quality drift in ongoing annotation projects by implementing statistical process control charts and automated quality checks, catching deviations 3 days earlier than manual review processes.
Improved annotation throughput without sacrificing quality by optimizing review workflows and implementing batch processing of similar content types, increasing daily output by 25% while maintaining 95%+ accuracy.
Addressed feedback loop inefficiencies by establishing structured peer review sessions and weekly calibration meetings, ensuring consistent application of annotation guidelines across all team members.

Jun 2021 - Sep 2024

3 years 4 months

Warsaw, Poland

Data Scientist

EPAM Systems

Solved financial document classification inconsistencies by developing a hierarchical annotation system with clear decision trees, improving classifier F1-score from 0.78 to 0.91 on banking transaction data from SAP and internal databases.
Addressed annotation scalability challenges for multi-language financial reports by implementing a distributed labeling platform with quality gates, processing 50K+ documents monthly with 94% consistency across English and German content.
Fixed model performance degradation in production by establishing continuous annotation pipelines for hard cases, reducing false positives in fraud detection by 22% while maintaining 99.9% recall on transaction monitoring systems.
Resolved training data quality issues by implementing schema validation and outlier detection in feature pipelines, decreasing data-related production incidents by 65% across client financial services applications.
Eliminated annotation backlog during project scaling by designing efficient batch processing workflows and priority queuing systems, maintaining SLA compliance despite 3x volume increases during quarterly reporting periods.
Improved cross-team annotation consistency by conducting weekly calibration sessions and developing detailed feedback documentation, achieving 0.88 inter-annotator agreement across distributed teams in different time zones.
Solved model interpretability challenges in client presentations by creating comprehensive annotation guidelines with business context, reducing explanation time from 45 to 15 minutes during stakeholder reviews.

Aug 2019 - Jun 2021

1 year 11 months

Warsaw, Poland

Senior MLOps

Fujitsu

Addressed manual annotation bottlenecks in manufacturing defect detection by implementing semi-automated labeling tools with human verification, reducing labeling time by 40% for CV models processing production line imagery.
Solved training data versioning chaos by establishing centralized annotation storage with metadata tracking, enabling reproducible model training across multiple manufacturing facility datasets.
Fixed quality inconsistencies in sensor data annotation by developing standardized labeling protocols and conducting train-the-trainer sessions, improving model accuracy by 15% on predictive maintenance tasks.
Resolved annotation tool reliability issues by migrating from custom scripts to containerized labeling applications, achieving 99.5% uptime for distributed annotation teams across three manufacturing sites.

Apr 2014 - Aug 2019

5 years 5 months

Japan

Machine Learning Engineer

Fujitsu

Solved initial data labeling challenges for early ML projects by developing structured annotation guidelines and quality check procedures, establishing foundation for reproducible model development.
Addressed limited training data availability by implementing data augmentation techniques and systematic labeling workflows, enabling successful deployment of first-generation recommendation systems.
Fixed annotation consistency issues across team members by creating detailed examples and edge case documentation, improving model performance stability during initial production deployments.
Resolved manual quality assurance bottlenecks by developing automated validation scripts for annotated datasets, reducing review time by 50% while maintaining high data quality standards.

Industries Experience

See where this freelancer has spent most of their professional time. Longer bars indicate deeper hands-on experience, while shorter ones reflect targeted or project-based work.

Experienced in Information Technology (8.5 years), Banking and Finance (3.5 years), and Manufacturing (3 years).

Information Technology

Banking and Finance

Manufacturing

Business Areas Experience

The graph below provides a cumulative view of the freelancer's experience across multiple business areas, calculated from completed and active engagements. It highlights the areas where the freelancer has most frequently contributed to planning, execution, and delivery of business outcomes.

Experienced in Information Technology (11.5 years), Quality Assurance (11.5 years), Product Development (5.5 years), and Business Intelligence (3.5 years).

Information Technology

Quality Assurance

Product Development

Business Intelligence

Summary

10+ years of experience building and deploying machine learning systems across manufacturing and financial services domains. Specialized in end-to-end MLOps implementation with expertise in data annotation quality assurance, factual accuracy evaluation, and content moderation workflows. Proven track record in establishing quality control processes for AI training data and implementing structured feedback systems for annotation teams. Combines deep technical ML expertise with practical experience in mentoring junior staff and conducting quality assurance reviews.

Skills

Annotation & Qa: Data Labeling, Quality Serving & Monitoring, Drift Control, Fact Checking, Content Moderation Detection, Performance Monitoring, A/b Testing
Mlops & Platforms: Mlflow, Kubeflow, Azure
Devops & Collaboration: Github Actions
Ml Infrastructure: Feature Stores, Model Registry, Docker, Kubernetes
Mentoring & Workshop Facilitation
Modeling: Classification, Nlp, Llm Evaluation, Classical Ml, Deep Learning
Languages: Python, Sql
Data & Features: Sql, Spark, Pandas, Data Validation, Schema Evolution

Languages

Japanese

Native

German

Advanced

English

Advanced

Education

Apr 2016 - Mar 2018

Tokyo University of Science

Master's of Computer Science · Computer Science · Japan

Apr 2010 - Mar 2014

Tokyo University of Science

Bachelor's of Computer Science · Computer Science · Japan

Certifications & licenses

Google Professional Machine Learning Engineer

AWS Certified Machine Learning - Specialty

DeepLearning.AI Natural Language Processing Specialization

Profile

Created

November 2025

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Frequently asked questions

Do you have questions? Here you can find further information.

Where is Hiroshi based?

Hiroshi is based in Warsaw, Poland.

What languages does Hiroshi speak?

Hiroshi speaks the following languages: Japanese (Native), German (Advanced), English (Advanced).

How many years of experience does Hiroshi have?

Hiroshi has at least 11 years of experience. During this time, Hiroshi has worked in at least 4 different roles and for 3 different companies. The average length of individual experience is 3 years and 10 months. Note that Hiroshi may not have shared all experience and actually has more experience.

What roles would Hiroshi be best suited for?

Based on recent experience, Hiroshi would be well-suited for roles such as: Senior Data Scientist, Data Scientist, Senior MLOps.

What is Hiroshi's latest experience?

Hiroshi's most recent position is Senior Data Scientist at Siemens.

What companies has Hiroshi worked for in recent years?

In recent years, Hiroshi has worked for Siemens, EPAM Systems, and Fujitsu.

Which industries is Hiroshi most experienced in?

Hiroshi is most experienced in industries like Information Technology (IT), Banking and Finance, and Manufacturing.

Which business areas is Hiroshi most experienced in?

Hiroshi is most experienced in business areas like Information Technology (IT), Quality Assurance (QA), and Product Development. Hiroshi also has some experience in Business Intelligence.

Which industries has Hiroshi worked in recently?

Hiroshi has recently worked in industries like Banking and Finance, Information Technology (IT), and Manufacturing.

Which business areas has Hiroshi worked in recently?

Hiroshi has recently worked in business areas like Information Technology (IT), Quality Assurance (QA), and Business Intelligence.

What is Hiroshi's education?

Hiroshi holds a Master in Computer Science from Tokyo University of Science and a Bachelor in Computer Science from Tokyo University of Science.

Does Hiroshi have any certificates?

Hiroshi has 3 certificates. These include: Google Professional Machine Learning Engineer, AWS Certified Machine Learning - Specialty, and DeepLearning.AI Natural Language Processing Specialization.

What is the availability of Hiroshi?

Hiroshi is immediately available for suitable projects.

What is the rate of Hiroshi?

Hiroshi's rate depends on the specific project requirements. Please use the Meet button on the profile to schedule a meeting and discuss the details.