Profileimage by Thomas Lautenschlaeger Senior Software Developer / Machine Learning Engineer /  / Python from Darmstadt

Thomas Lautenschläger

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Last update: 29.04.2024

Senior Software Developer / Machine Learning Engineer / / Python

Graduation: M.Sc. Computer Science - TU Darmstadt
Hourly-/Daily rates: show
Languages: German (Native or Bilingual) | English (Full Professional) | Spanish (Elementary)

Attachments

cv-thomas-lautenschlaeger_050224.pdf

Skills

[ENG]

Welcome to my profile! My journey has taken me through diverse projects encompassing core software development, the intricacies of machine learning model creation, lifecycle management, and comprehensive data analysis. My dedication to every phase, from conceptualization to execution, has positioned me at the vanguard of technology, ensuring timely and innovative solutions for clients. I welcome new opportunities and challenges, so please don't hesitate to reach out.

[DE]

Willkommen auf meinem Profil! Meine Reise führte mich durch vielfältige Projekte, von der Grundlage der Softwareentwicklung bis hin zu den Feinheiten der Erstellung von Machine-Learning-Modellen, Lebenszyklusmanagement und umfassender Datenanalyse. Meine Hingabe in jeder Phase, von der Konzeption bis zur Umsetzung, hat mich zu den neusten Technologien geführt und gewährleistet pünktliche und innovative Lösungen für meine Kunden. Ich freue mich über neue Gelegenheiten und Herausforderungen, also zögern Sie bitte nicht, Kontakt aufzunehmen.


Core Competencies:
  • Algorithm Implementation: Efficiently implements computationally intensive algorithms for scalability.
  • Market Analysis: Specializes in time series and stock market analytics.
  • Deep Learning Expertise: Proficient in deep learning and ensuring numerical stability.
  • Advanced Data Analytics: Experienced in Bayesian optimization and analyzing high-dimensional, non-linear data.
  • Data Visualization: Capable of visualizing, presenting, and documenting complex data and tasks.
  • Process Automation: Strong in automating processes and verifying complex algorithms.
  • Research Aptitude: Demonstrates effectiveness in research environments.
  • Reinforcement Learning: Knowledgeable in reinforcement learning with education from IAS Darmstadt, TU Darmstadt.
  • Advanced Modelling: Experienced in multi-armed bandits, model predictive control, and probabilistic modelling.
  • Research Trends: Stays current with latest research developments.
  • Feature Extraction & Analysis: Proficient in feature extraction, clustering, and classification algorithms.
  • Version Control: Competent in using Git and other versioning systems.
  • Tool Proficiency: Skilled in various analytical tools and frameworks.

Professional Experience:
  • Quantitative Analyst/Machine Learning Scientist, Privately Managed Fund: Utilized machine learning techniques for fund management.
  • Developer, Analytics Dashboard: Developed an analytics dashboard using Django, Flask, and MongoDB.
  • Database Developer: Designed and developed a relational database (PostgreSQL) with API access via FastAPI. Security standards according to Azure best-practices.
  • Machine Learning Engineer: Built a robust stock analytics algorithm for real-time anomaly detection and reporting (PyTorch, Pandas).
  • Research Associate: Solved a non-linear optimization task using deep learning architectures for an LED lamp setting optimization project (Tensorflow, Matlab).
  • Robotics Data Scientist: Worked extensively with robotics data and implemented classic control and reinforcement learning algorithms.
  • Data Scientist, Twitter Sentiment Analysis: Conducted real-time sentiment analysis on Twitter data using spaCy.
  • Data Engineer, Multiple Projects: Developed several data pipelines with distributed processing and data provisioning via Rest API access (Google Cloud, FastAPI, Docker, Terraform).
  • Time Series Data Specialist: Led numerous projects involving time series data.
  • Software Development: Developed several software projects from initial design to operations including CI/CD pipelines, Kubernetes deployment, integrating MLOps production principles and monitoring the live runtime.
     
Tools & Frameworks
  • AWS
  • Azure
  • Docker
  • FastAPI
  • Flask
  • GitHub Actions
  • GitLab CI/CD
  • Git
  • Google Cloud
  • JAX
  • Jupyter
  • Keras
  • Kubernetes
  • MLFlow
  • Numpy
  • Pandas
  • PostgreSQL
  • Pyro
  • Python
  • PyTorch
  • Scikit-learn
  • SciPy
  • SQL
  • Stan
  • TensorFlow / TensorFlow Probability
  • Terraform

Project history

08/2022 - Present
Python Software Development - Machine Learning Engineering
(Energy, water and environment, >10.000 employees)

  • Conceptual Design & Implementation: Focused on the development of scalable software solutions for monitoring the German electrical grid.
  • DevOps Practices: Employing DevOps methodologies for efficient and effective project lifecycle management.
  • Machine Learning & Rule-Based Systems: Integrating advanced machine learning techniques and rule-based mechanisms for enhanced grid analysis.
  • Project Rollout: Leading the rollout of smart energy initiatives across Germany.
  • Team Collaboration: Working within a Scrum framework, promoting agile and collaborative team dynamics.
  • Innovative Problem-Solving: Driving innovation in tackling complex challenges in grid monitoring.
  • Stakeholder Engagement: Actively communicating with stakeholders to align software development with organizational goals and grid requirements.

08/2023 - 03/2023
Developed an internal SDK for Data Scientists
(Energy, water and environment, >10.000 employees)

  • SDK Development: Creating a Python SDK for internal sensor data access, optimized for data scientists.
  • User-Centric Design: Ensuring the SDK is intuitive and meets the specific needs of data scientists.
  • Authentication Standards: Implementing secure authentication aligned with Azure standards.
  • Best Practice Adoption: Establishing the SDK as a Mono Repository exemplar for Python projects in the organization.
  • Documentation & Support: Providing comprehensive documentation and support for the SDK users.
  • Continuous Integration/Continuous Deployment (CI/CD): Implementing CI/CD practices for efficient development and deployment of the SDK.
  • Collaboration & Feedback Integration: Collaborating with end-users and stakeholders to continuously refine and enhance the SDK functionalities.

02/2020 - 08/2022
Machine Learning Scientist/Engineer
(Banks and financial services, < 10 employees)

  • Software Design & Development: Creating investment software with advanced backtesting and analysis functionalities.
  • Strategic Analysis: Delivering insights into the performance of various investment strategies.
  • Strategy Development: Contributing to the design and creation of innovative investment strategies.
  • Trade Execution: Applying analytics software results to execute trades and manage portfolios effectively.
  • Data Integration: Ensuring seamless integration of diverse financial data sources to enhance the analytical capabilities of the software.
  • Continuous Improvement: Iteratively refining the software based on market trends, user feedback, and performance metrics.

Local Availability

Open to travel worldwide

Covered by Exali's professional indemnity insurance

The freelancer is covered by a reliable insurance provider that offers protection against common risks associated with digital and IT professions (damage claims, third-party cyber damage, etc.).

Profileimage by Thomas Lautenschlaeger Senior Software Developer / Machine Learning Engineer /  / Python from Darmstadt Senior Software Developer / Machine Learning Engineer / / Python
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