Gwang Jin (Josephus) Kim-Data Scientist / Applied AI, Automation & Data Systems Researcher
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Experience
Data Scientist / Applied AI, Automation & Data Systems Researcher
Independent
- Built and explored applied GenAI, RAG, GraphRAG, local LLM, agentic AI and document-intelligence prototypes for structured analysis, evidence extraction, semantic search, technical reasoning and decision-useful reporting
- Developed private local-LLM workflows and AI system patterns focused on privacy, reproducibility, reviewability, low-cost inference and practical user control
- Built reproducible Python/R workflows for data analysis, automation, API-driven tooling, validation logic, technical documentation and AI-assisted software development
- Designed workflows around explicit assumptions, traceable inputs, reviewable outputs and failure-mode awareness rather than black-box “looks good” demonstrations
- Supported RAHN AG in a chemical/regulatory environment with data extraction and processing around WERCS, a regulatory application for chemical product and compliance data
- Explored complex application/database schemas and wrote nested SQL queries to extract information for mixture calculations, component relationships, regulatory rules and reporting logic
- Continued hands-on development in Git/GitHub/GitLab/Bitbucket, Docker/Linux deployment patterns, REST/API workflows, error handling, technical writing and fast AI-assisted prototyping
- Built technical writing and documentation workflows that turn complex systems into clear runbooks, checklists, decision notes and user-facing explanations
Associate Expert Digital Solutions
Novartis AG (via Actalent / Allegis Group)
- Built machine-learning and deep-learning models for bioprocess and spectral data, including CNN-based prediction of analytical readouts from Raman/spectral inputs under realistic small-data constraints
- Designed validation logic, compared model behaviour, analyzed prediction errors and treated model failures as useful signals for data, preprocessing and experimental limitations
- Developed Python, Streamlit, Flask and REST-API-based tools for process monitoring, data retrieval, visualization, reporting and operational decision support
- Created a Python wrapper for an internal REST API, making live process and bioreactor data easier to access, inspect, analyze, reuse and explain
- Automated data extraction, cleaning, reporting, backup and monitoring workflows using Python, PowerShell, Bash, SQL and structured documentation
- Worked in a regulated biopharma R&D / antibody-production environment where model outputs, dashboards and data workflows had to be understandable, traceable and useful to technical users
- Supported database-backed application/data workflows around Lucullus, a bioreactor software system used to collect sensor data and support on-site bioreactor operations
- Worked closely with wet-lab, dry-lab, IT, automation and support teams to clarify workflow problems, validate data behaviour and convert analytical needs into usable tools
- Helped colleagues and senior data scientists with R/Python scripting, deep learning, model interpretation, practical validation and translating modeling ideas into workflows others could use
Postdoctoral Bioinformatician / Data Scientist
University of Freiburg, SFB 992 “Medical Epigenetics”
- Led end-to-end analysis of large biomedical datasets, including single-cell RNA-seq, bulk RNA-seq, ChIP-seq and ATAC-seq: QC, preprocessing, differential analysis, annotation, visualization, interpretation and publication support
- Built reproducible Linux/HPC pipelines for large datasets using Bash, R/Bioconductor, Python, Git, conda environments, Make-style automation and job schedulers
- Processed structured and semi-structured data from sample sheets, genome annotations, result tables, metadata, large text/annotation files and tool outputs
- Developed and evaluated ML-oriented approaches for genomics questions, including feature engineering, dimensionality reduction, statistical validation, failure-mode analysis and careful interpretation
- Designed analysis strategies where conclusions had to survive QC, biological plausibility checks, alternative explanations and collaborator review
- Advised professors, postdocs, PhD students, MSc students and medical students on analysis design, validity limits, reproducibility, troubleshooting and defensible interpretation
- Wrote practical HPC, pipeline and workflow documentation that improved usability beyond my own immediate research group
- Contributed to peer-reviewed publications, including work featured as a Nature Cell Biology cover
Molecular Biology Researcher – PhD, Molecular Medicine
University of Freiburg
- Conducted publication-grade research in human genetics, developmental biology, disease mechanisms, regulatory genomics, molecular biology and experimental model systems
- Worked with structured experimental documentation, sequence/annotation resources, experimental datasets, control logic, failure sources and reproducible interpretation
- Built the scientific foundation I still use in ML work: experimental design, controls, causality, confounding, data quality, failure modes and cautious interpretation
- Worked on gene regulation, Dicer/SOX9/SOX8-related biology and genetic disease contexts connected to high-impact publications
Master Student and Assistant Student Worker, Molecular Medicine
University of Freiburg
- Built early quantitative and experimental discipline through biomathematics / biostatistics, qRT-PCR, genotyping, recombinant virology, immunohistology, primary cell culture and structured data interpretation
- Worked in genetics, internal medicine and virology research environments with a strong focus on evidence, controls, data quality and reproducibility
Industry Experience
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Experienced in Education, Biotechnology, Chemical, Information Technology, and Pharmaceutical.
Business Area Experience
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Experienced in Research and Development, Information Technology, and Quality Assurance.
Summary
Applied Machine Learning Engineer and PhD-trained natural scientist with 10+ years of data-intensive modeling, scientific computing, automation and machine-learning experience, including direct Novartis R&D work in a regulated biopharma environment. Strong hands-on background in Python, deep learning, CNNs, model evaluation, data pipelines, experimental design, failure analysis, scientific validation and production-adjacent digital tooling.
Strong fit for Vision-Language Model / multimodal AI projects where models must be evaluated carefully, improved iteratively and connected to real-world use cases rather than treated as impressive demos. My core strength is the combination of ML implementation, data-centric experimentation, benchmark design, robustness thinking, and the scientific habit of asking why a model fails before claiming that it works.
For FRATCH’s conversational driving / parking use case, I would position myself as an applied ML builder who can support VLM-based solutions, datasets, evaluation frameworks, prompting / fine-tuning strategies, failure analysis, automated testing, and collaboration with integration or domain engineers. I have not worked in automotive ADAS directly, but I have repeatedly worked where sensor-like data, complex systems, model reliability and domain constraints must meet.
Skills
Applied Ml / Deep Learning: Supervised Learning, Cnns, Classification, Regression, Model Adaptation, Model Evaluation, Failure Analysis, Error Taxonomy, Small-Data Modeling, Robustness Checks And Cautious Interpretation
Vision / Multimodal Ai Readiness: Computer-Vision Fundamentals, Image/Signal-Style Data Thinking, Multimodal Ai, Vision-Language Models, Foundation-Model Evaluation, Prompt-Based Adaptation, Fine-Tuning Concepts And Model-Output Validation
Evaluation And Experimentation: Benchmark Design, Experiment Tracking Discipline, Ablation-Style Thinking, Performance Metrics, Edge-Case Discovery, Failure-Mode Analysis, Model Comparison And Data-Centric Improvement Loops
Data Engineering For Ml: Dataset Construction, Preprocessing, Annotation-Aware Thinking, Data Validation, Feature Engineering, Versioned Inputs, Reproducible Pipelines, Automated Reporting And Quality-Control Gates
Python Ml Implementation: Python, Pytorch, Tensorflow/Keras, Scikit-Learn, Xgboost, Pandas/Numpy, Jupyter, Streamlit/Flask, Rest Apis, Git, Linux And Reproducible Environments
Llm / Genai Systems: Llms, Rag, Graphrag, Embeddings, Document Intelligence, Prompt Engineering, Context Engineering, Structured Outputs, Hallucination Reduction And Reviewable Ai Workflows
Real-World Model Reliability: Translating Ambiguous Domain Requirements Into Measurable Tests, Identifying Data Gaps, Documenting Assumptions, Explaining Uncertainty And Building Tools Others Can Inspect And Reuse
Cross-Functional Delivery: Working With Scientists, Engineers, It/Support Teams And Domain Experts; Translating Domain Needs Into Technical Concepts, Experiments, Reports And Usable Tools
Machine Learning / Deep Learning: Deep Learning, Cnns, Classification, Regression, Supervised Learning, Model Validation, Error Analysis, Robustness Checks, Explainability, Uncertainty Communication, Small-Data Modeling, Scikit-Learn, Xgboost, Pytorch, Tensorflow/Keras
Vision / Multimodal Ai: Vision-Language Models, Multimodal Ai, Computer-Vision Fundamentals, Image/Signal-Style Data, Foundation Models, Prompt-Based Adaptation, Fine-Tuning Concepts, Model-Output Evaluation, Edge-Case Analysis
Evaluation / Experimentation: Benchmark Design, Datasets For Model Assessment, Experiment Design, Metrics, Failure Taxonomies, Data-Centric Improvement, Validation Gates, Reproducible Reports, Automated Evaluation Concepts
Data Engineering: Dataset Construction, Data Cleaning, Preprocessing, Annotation-Aware Workflows, Metadata Handling, Feature Engineering, Data Quality, Versioned Inputs, Pipelines, Structured Exports, Monitoring-Ready Workflows
Programming: Python, R, Sql, Bash, Powershell, Git, Linux, Macos, Windows, Common Lisp, Julia Exposure, Javascript Exposure
Python Ml/Data Stack: Pandas, Numpy, Scipy, Matplotlib, Plotly, Jupyter, Scikit-Learn, Xgboost, Pytorch, Tensorflow/Keras, Streamlit, Flask, Rest Apis
Llm / Genai Systems: Llms, Genai, Rag, Graphrag, Embeddings, Semantic Search, Prompt Engineering, Context Engineering, Local Llms, Document Intelligence, Structured Outputs, Hallucination Reduction, Ai Evaluation
Databases / Structured Data: Sql, Relational Databases, Postgresql/Mysql/Mariadb, Sql Server Exposure, Schema Exploration, Joins, Nested Sql, Data Extraction, Data-Quality Checks And Reporting Workflows
Deployment-Adjacent Tooling: Docker Exposure, Linux Deployment Patterns, Reproducible Environments, Rest/Api Integration, Logging-Oriented Thinking, Monitoring Logic, Technical Documentation, Ci/Cd Exposure
Languages
Education
University of Freiburg
PhD, Molecular Medicine · Molecular Medicine · Freiburg im Breisgau, Germany
University of Freiburg
MSc / Diploma, Molecular Medicine · Molecular Medicine · Freiburg im Breisgau, Germany
Certifications & licenses
Agile Software Development: Scrum For Developers
LinkedIn Learning
Data Analysis With Python
freeCodeCamp
Javascript Algorithms And Data Structures
freeCodeCamp
Neo4j Graph Data Science Certification
Neo4j
Scientific Computing With Python
freeCodeCamp
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