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Senior Data Scientist

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  • 08.09.2025
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Kurzvorstellung

Expert in LLM, RAG, Machine Learning, AI, Recommender System, Pricing Engines, Computer Vision

Geschäftsdaten

 Gewerbetreibend
 Steuernummer bekannt

Qualifikationen

  • Anomaly Detection
  • Computer Vision1 J.
  • Data Science5 J.
  • Document Retrieval
  • Graph Theory
  • Large Language Models1 J.
  • Maschinelles Lernen
  • NLP
  • Recommender Systems

Projekt‐ & Berufserfahrung

Senior Data Scientist - Text Classification, NLP
Kundenname anonymisiert, Koeln
5/2025 – 6/2025 (2 Monate)
Chemieindustrie
Tätigkeitszeitraum

5/2025 – 6/2025

Tätigkeitsbeschreibung

Objective: classifying call trascription with a multi-class classifier. The labels were nested in a hierarchy.
Achievement: The solution involved an emsamble of binary classifiers, working with a class hieararchy (hierarchical classifier, OneVsRestClassifier). The preprocessing involved topic extraction (keywords) to enrich the features, as well as obtaining a number of embeddings (OpenAI API, QWEN, etc.)

Eingesetzte Qualifikationen

Data Science, Large Language Models, Machine Learning, Machine Learning Engineer

Senior Data Scientist
Kundenname anonymisiert, Munich
11/2024 – 6/2025 (8 Monate)
IT & Entwicklung
Tätigkeitszeitraum

11/2024 – 6/2025

Tätigkeitsbeschreibung

Objective: To create a multi-agentic system, supported by a Knowledge Graph, that
automates the process of drafting a research paper. The system used multiple experts
(OpenAI models) that ”collaborated” during the process of document drafting. The
whole process was supported by a Knowledge Graph out of which we extracted useful
information.

Eingesetzte Qualifikationen

Data Science, Machine Learning Engineer, Large Language Models, Natural Language Processing

Senior Data Scientist - Computer Vision
Kundenname anonymisiert, Tel Aviv
2/2024 – 2/2025 (1 Jahr, 1 Monat)
IT & Entwicklung
Tätigkeitszeitraum

2/2024 – 2/2025

Tätigkeitsbeschreibung

Objective: given a spectral image that depicts the value of the sensors’s readings,
classify the signals and identify novelties (anomalies).
Approach: Given that there was not sufficient labeled data, I had to rely on self-
supervised machine learning paradigms. To satisfy the customer’s request for fast
processing, I utilized one of the YOLO architectures. The system was developed
with the mmyolo framework.

Eingesetzte Qualifikationen

Data Science, Machine Learning Engineer, Computer Vision, Machine Learning

Senior Data Scientist - RAG system, chatbot
Kundenname anonymisiert, Munich
2/2024 – 9/2024 (8 Monate)
IT & Entwicklung
Tätigkeitszeitraum

2/2024 – 9/2024

Tätigkeitsbeschreibung

Objective: an LLM chatbot to help with HR-related inquiries.
Approach: I led the development of an advanced Retrieval-Augmented Generation
(RAG) system aimed at improving HR data retrieval processes. This system utilizes
the Milvus Vector Similarity Search Database and OpenAI’s API to efficiently source
and integrate extensive HR-related data, enabling it to respond to a broad spectrum
of HR inquiries.
Used: Langchain, Langraph, MCP, Agentic frameworks, dspy

Eingesetzte Qualifikationen

Data Science, Large Language Models, Machine Learning, Natural Language Processing

Senior Data Scientist - Recommender System
Kundenname anonymisiert, Munich
8/2021 – 9/2023 (2 Jahre, 2 Monate)
Medienbranche
Tätigkeitszeitraum

8/2021 – 9/2023

Tätigkeitsbeschreibung

Developing a Recommender System powering one of the largest Audio on Demand platforms in Germany.

Eingesetzte Qualifikationen

Data Science, Machine Learning Engineer, Machine Learning

Senior Data Scientist
Kundenname anonymisiert, Munich
8/2021 – 7/2023 (2 Jahre)
IT & Entwicklung
Tätigkeitszeitraum

8/2021 – 7/2023

Tätigkeitsbeschreibung

Design and implementation of a Recommender System powering one of the most popular German video- and audio-on-demand platforms, with hundreds of thousands item in the catalogue-pool.

Eingesetzte Qualifikationen

Data Science

Senior Data Scientist - Anomaly Detection
Kundenname anonymisiert, Berlin
4/2021 – 8/2021 (5 Monate)
Banken
Tätigkeitszeitraum

4/2021 – 8/2021

Tätigkeitsbeschreibung

Objective: Fraud detection.
Approach: I designed and developed a fraud detection model for a Middle-Eastern
Buy-Now Pay-Later platform. A critical step for this client involved data pre-
processing, during which I employed a graph-based approach to identify cliques of
fraudsters. This was the first successful machine learning project for this client.

Eingesetzte Qualifikationen

Data Science, IDS (Intrusion Detection System), Machine Learning

Senior Data Scientist - Debt Collection
Kundenname anonymisiert, Hamburg
9/2019 – 4/2020 (8 Monate)
Banken
Tätigkeitszeitraum

9/2019 – 4/2020

Tätigkeitsbeschreibung

Objective: To predict the likelihood of loan repayment by bank customers, aiding
in their segmentation for tailored communication strategies (email, SMS, or phone
calls).
Achievement: Created a behavioral scoring machine learning model, now incorporated
into Receeve’s collection approach.

Eingesetzte Qualifikationen

Data Science, Machine Learning Engineer, Machine Learning

Senior Data Scientist - Pricing Engine
Kundenname anonymisiert, New York
4/2018 – 12/2018 (9 Monate)
Dienstleistungsbranchen (Service)
Tätigkeitszeitraum

4/2018 – 12/2018

Tätigkeitsbeschreibung

Objective: To predict vehicle transportation prices, providing dispatchers with a
reliable basis for pricing.
Achievements: Enhanced the machine learning model’s accuracy by approximately
40% through advanced feature engineering and implementing a two-tiered regression
strategy, combining residual and standard regression techniques powered by XGBoost.

Eingesetzte Qualifikationen

Data Science, Machine Learning Engineer

Weitere Projekt‐ & Berufserfahrung anzeigen

Zertifikate

Design and Implementation of State of the Art Recommender Systems
2022

Ausbildung

P. in Computer Science
P. in Computer Science
2012
Konstanz

Über mich

Dr. M. K. holds a P. in Computer Science and brings extensive experience in Data Science, Machine Learning, and AI systems. His core expertise includes Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Recommender Systems, Graph Neural Networks, Knowledge Graphs, and classical ML algorithms. He led the development of the recommender system for ARD Audiothek and has delivered a wide range of projects, including anomaly detection, pricing engines, and agentic AI applications.
He is proficient in state-of-the-art ML frameworks such as PyTorch, TensorFlow, PyG, and mmyolo, and has hands-on experience with tools like Milvus, LangChain, LlamaIndex, and GitHub Actions (CI/CD). His cloud experience spans AWS (SageMaker, Lambda, EC2, ECS), GCP (BigQuery), and Azure (Databricks). He is well-versed in building scalable ML pipelines and has worked with vector search engines, Kafka, Snowflake, Redis, PostgreSQL, and Neo4j.

Programming skills include Python (with scikit-learn, XGBoost, Transformers), Java, C/C++, and MATLAB. He is fluent in English and German (C1/C2), and a native speaker of Bosnian. Dr. Klimenta follows agile methodologies (Scrum, Kanban) and has worked with international teams and organizations such as Bayerischer Rundfunk, Yewno, and Visteon. In addition to his technical career, he is also a published author of literary fiction

Weitere Kenntnisse

Recommender Systems, Data Science, Machine Learning, Deep Learning, Computer Vision, Algorithm Engineering, Pricing Engines

Persönliche Daten

Sprache
  • Englisch (Muttersprache)
  • Deutsch (Fließend)
Reisebereitschaft
auf Anfrage
Arbeitserlaubnis
  • Europäische Union
Home-Office
bevorzugt
Profilaufrufe
489
Alter
38
Berufserfahrung
16 Jahre und 1 Monat (seit 11/2009)
Projektleitung
8 Jahre

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