Senior Machine Learning & Data Engineer
- Verfügbarkeit einsehen
- 0 Referenzen
- 75‐100€/Stunde
- 74889 Sinsheim (Elsenz)
- auf Anfrage
- de | ru | en
- 29.04.2024
Kurzvorstellung
Qualifikationen
Projekt‐ & Berufserfahrung
12/2023 – 3/2024
Tätigkeitsbeschreibung
- Development of a recommendation engine on Big Data
- Design of an architecture for a feature store
Amazon Web Services (AWS), Apache Spark, Big Data, Business Intelligence (BI), Continuous Delivery (CDE), Datenmodellierung, DevOps, Maschinelles Lernen, mySQL, Pandas DataFrame, Python, Software Architektur / Modellierung, SQL
10/2022 – 6/2023
Tätigkeitsbeschreibung
- Led the development of a secure, scalable data storage solution, facilitating efficient model building and R&D efforts.
- Pioneered data augmentation strategies for ML models, enhancing the predictive accuracy for firms with limited data points.
- Authored compliance tests against US regulations for bias and discrimination in predictive hiring models.
- Contributed to the growth of the company's customer base by developing tailored predictive models for small
firms.
Data Science, Entscheidungsbaum Lernen, Kubernetes, Maschinelles Lernen, Pandas DataFrame, Predictive analytics, Python, Software Architektur / Modellierung
11/2019 – 9/2022
Tätigkeitsbeschreibung
- Championed the adoption of PySpark, reducing KPI computation time from 24 hours to 18 minutes, and subsequently spearheaded a company-wide workshop on Spark implementation, establishing it as a standard for data-intensive projects.
- Served as the technical lead for the pallet-loading optimization project, orchestrating a solution that resulted in multi-million euro savings by enhancing operational efficiency and reducing truck loading times.
- Automated object detection on video data, transitioning from a 2-month manual labeling process to an automated, minute-scale operation, significantly accelerating the client's data science initiatives.
- Organized and led an internal developer conference, orchestrating 42 presentations with 79 speakers, which cultivated a strong culture of knowledge sharing and innovation across the organization.
- Delivered 6 comprehensive knowledge-sharing sessions on security and vulnerability tools, elevating software quality and establishing new best practices within the engineering team.
- Authored a comprehensive OAuth 2.0 guide for data scientists, which was adopted as a company standard, ensuring secure and standardized authentication practices across all projects.
- Contributed to the development and refactoring of Proof of Concept (PoC) applications, the creation of CI/CD pipelines using Kubernetes, and the enhancement of operability through sophisticated logging, monitoring, and alerting systems.
- Drove analytics optimization by refining model parameters and algorithms, and played a pivotal role in the technical recruitment process, bolstering the team's expertise and efficiency.
- Led the meticulous documentation of application workflows and operational processes, substantially improving
the AI department's knowledge management and procedural clarity.
Data Science, Apache Spark, PostgreSQL, SQL, Maschinelles Lernen, Objekterkennung, Semantic Segmentation, Qualitätsmanagement / QS / QA (IT), Softwarequalität, Python, Kubernetes, Kostenoptimierung, Technische Projektleitung / Teamleitung
Ausbildung
Ruprecht-Karls University Heidelberg
Heidelberg
Über mich
As an experienced ML and Data Engineer, I offer a broad range of knowledge in models, algorithms, and optimization techniques. My expertise spans various areas of ML – from Supervised & Unsupervised Learning to specialized fields such as Reinforcement & Transfer Learning, Neural Networks, Regularization/Normalization Methods, Interpretability Tools, and Anomaly Detection. My expertise also includes MLOps, with proficiency in Kubernetes, Airflow, and CI/CD tools, enabling me also to industrialize and scale projects to production.
Passionate about solving complex problems, I aim to deliver measurable results and real value for your business. If your requirements extend beyond the mentioned technologies, do not hesitate to contact me. I look forward to working with you on exciting projects and enhancing your team with my expertise.
Deutsch:
Als erfahrener ML- und Data Engineer biete ich eine breite Palette an Kenntnissen in Modellen, Algorithmen und Optimierungstechniken. Mein Fachwissen erstreckt sich über verschiedene Bereiche des maschinellen Lernens – von Supervised & Unsupervised Learning bis hin zu spezialisierten Feldern wie Reinforcement & Transfer Learning, Neural Networks, Regularisierungs-/Normalisierungsmethoden, Interpretierbarkeitstools und Anomalieerkennung. Meine Expertise schließt auch MLOps ein, mit Kenntnissen in Kubernetes, Airflow und CI/CD-Tools, was es mir ermöglicht, Projekte auch zu industrialisieren und effizient in die Produktion zu skalieren.
Leidenschaftlich daran interessiert, komplexe Probleme zu lösen, strebe ich danach, messbare Ergebnisse und echten Mehrwert für Ihr Unternehmen zu liefern. Falls Ihre Anforderungen über die genannten Technologien hinausgehen, zögern Sie nicht, mich zu kontaktieren. Ich freue mich darauf, mit Ihnen an spannenden Projekten zu arbeiten und Ihr Team mit meiner Expertise zu bereichern.
Weitere Kenntnisse
Python, SQL, Apache Spark, Kubernetes, Azure/ GCP/AWS, Red Hat Openshift, REST, FastAPI/ Flask, ETL, Git, Airflow, Databricks, Splunk, Prometheus, Docker, ELK Stack, Keras, Grafana, TensorFlow, NumPy, scikit-learn, Pandas, Tableau, ArcGIS, Jira, Confluence
Code Quality:
Analysis, Styleguides, Webinars (Best practices, Tools, Techniques, Patterns, Detailed breakdown (also) for (big) codebases)
ML:
- Supervised Learning: Linear Regression, Logistic Regression, Support Vector Machines (SVM), Decision Trees, Neural Networks, k-Nearest Neighbors (k-NN), Naive Bayes, ElasticNet, Gradient Boosting Machines (GBMs), XGBoost, Random Forests
- Unsupervised Learning:
- Clustering: K-Means, Hierarchical Clustering, K-Medoids, DBSCAN, OPTICS, Mean Shift Clustering, Gaussian Mixture Models
- Dimensionality Reduction: Principal Component Analysis (PCA), t-SNE, Laplacian Eigenmaps, Kernel PCA
- Other: Autoencoders, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs)
- Reinforcement Learning: Actor-Critic Methods, Monte Carlo Tree Search, AlphaZero, Multi-Armed Bandit
- Transfer Learning: Fine-tuning, Domain Adaptation
- Ensemble Learning: Bagging, Boosting, Stacking
- Neural Network Architectures:
- Feedforward: Multi-Layer Perceptrons (MLP)
- Convolutional: Convolutional Neural Networks (CNNs), U-Net, Residual Networks (ResNets)
- Recurrent: Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), Gated Recurrent Units (GRU), Bidirectional RNNs
- Transformers: Attention Models, Transformer Models, BERT, RoBERTa, ALBERT, GPT-2, GPT-3
- Optimization Techniques: Gradient Descent, Stochastic Gradient Descent, Mini-Batch Gradient Descent, Momentum, Adagrad, Adam Optimizer, Bayesian Optimization, Simulated Annealing
- Regularization & Normalization: Dropout, DropConnect, Batch Normalization, Layer Normalization, Instance Normalization, Group Normalization, Weight Normalization
- Embeddings: Word2Vec, Triple2Vec
- Interpretability Tools: SHAP, LIME, Integrated Gradients
- Anomaly Detection: One-Class SVM, Isolation Forest
Persönliche Daten
- Deutsch (Muttersprache)
- Russisch (Muttersprache)
- Englisch (Fließend)
- Europäische Union
- Vereinigte Staaten von Amerika
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