Data Science Experts in Berlin
in minutes from over 15,000 CVs with the power of AI.Hire experts who turn data into forecasts, dashboards, and decision support with Python, SQL, and modern machine learning workflows. From model design to analytics delivery, get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Berlin, who have recently used Data Science
Dmitry Pankov
Last position:
Freelance Digital Marketing Analyst at Freelance
- Marketing Strategy: Lead the end-to-end analysis and evaluation of cross-channel marketing campaigns across the entire Customer Journey. My focus is identifying optimization potential and deriving clear, actionable recommendations that drive measurable business impact.
- Data Science & AI: Advanced predictive modeling (Churn, LTV), market basket analysis, clustering, and real-time AI-powered audience discovery utilizing RAG/LLMs.
- Marketing Analytics & Measurement: End-to-end attribution analysis, Marketing Mix Modeling (MMM), audience segmentation, conversion path analysis, and A/B testing across all major platforms.
- Data Engineering & Reporting: Designing and managing robust, multi-platform data pipelines (BigQuery, GCP) for data consolidation, automated dashboard generation, and critical API integrations.
Deepak Mishra
Last position:
Lead ML Platform Engineer at Billie GmbH
- Mentor team of 6 ML platform engineers through weekly 1:1s, technical design reviews, and best practices, improving team velocity by 35% through structured sprint planning and skill development programs
- Define 2025–2026 ML platform roadmap in collaboration with Data Science, Cloud Engineering, and Product teams, prioritizing automated model governance, cost attribution systems, and multi-environment deployment strategies
- Partner with Data Science, SRE, and Product stakeholders to align ML platform capabilities with business objectives, reducing data scientist deployment friction by 60% through self-service platforms
- Architect and deliver production-grade MLOps platform supporting 50+ models in production with automated promotion pipelines, versioning, and rollback capabilities, achieving 99.5% platform uptime SLA
- Design distributed ML pipeline architecture using Metaflow and Argo Workflows (Vertex Pipelines-compatible), reducing model training time by 30% and deployment cycles from 2 weeks to 3 days through full CI/CD automation
- Build containerized ML services on Kubernetes with auto-scaling policies, resource quotas, and multi-tenancy isolation, optimizing infrastructure costs by $180K annually (25% reduction)
- Implement monitoring, alerting, and performance tracking using Prometheus, Grafana, and custom instrumentation, reducing model debugging time by 50% and establishing model performance SLOs
- Lead development of RAG-based document intelligence platform using LangChain, LangGraph, and vector databases, implementing agentic AI workflows for automated financial document processing
- Implement Infrastructure-as-Code using Terraform for reproducible environment provisioning and GitOps workflows, reducing infrastructure drift incidents by 80%
- Design role-based access control for ML platform, implement model lineage tracking, and establish audit trails for regulatory compliance aligned with enterprise IAM best practices
Sejal Vaidya
Last position:
Data & ML Engineering at Consulting
- Fractional leadership; consulting growth-stage startups and scale-ups on data strategy, ML products, and platform foundations
- Building decisioning systems for growth, personalization, & product experimentation, across e-Commerce, Digital Health, Energy, and Logistics
- Exploring Agentic AI & LLM-based tooling for production readiness patterns
Wolfram Knan
Last position:
AI / Machine Learning Engineer (Projects & Applied AI) at UNIVERSITÉ PARIS 1 PANTHEON-SORBONNE & LIORA
- Designed and implemented a hybrid recommendation system (content-based + collaborative filtering)
- Built end-to-end ML pipelines including data processing, feature engineering, model training, and evaluation
- Developed RAG-based LLM systems using LangChain and vector databases for semantic search and knowledge retrieval
- Established MLOps workflows with MLflow for experiment tracking, versioning, and deployment readiness
- Implemented deep learning models (computer vision & classification) using PyTorch and TensorFlow
Muzamal Ali
Last position:
Data Scientist / AI Consultant at HelmX
- Delivered AI and data science solutions, including LLM-based chatbots and data pipelines, improving operational efficiency.
- Collaborated on product features, achieving measurable impact and maintaining strong client relationships.
Birte Loeckel
Last position:
Product & Agile Consultant at self employed
- Support teams, companies and individuals to become excellent Product Managers
- Build up the right organization to create customer and business value
Marc Fritze
Last position:
Interim Talent Acquisition Manager at doctari
- Building a cross-functional product team to develop a super app
- Advising and mentoring to support the team and provide input (technical & soft skills)
Raphael Mankopf
Last position:
Founder / Quant Developer at Market Maker
- Crypto quant strategy development, automated trade execution, onchain data client (Ethereum / Solana)
- Data and trade architecture development for liquidity provision
Mathias Wilhelm
Last position:
Implementation of an on-premise OCR solution with information extraction at Mindhopper GmbH
- Insurance service provider*
Challenge: Business-critical documents were processed through external OCR providers, with ongoing costs, dependency, and data privacy risks for sensitive insurance data.
Implementation:
- Architecture and production implementation of an on-premise OCR solution with full data ownership
- Methods for recognizing document structures as the basis for automated further processing
- ML-, NLP-, and LLM/VLM-based information extraction, especially from invoices and quotations
Success: Replaced external providers: full data ownership, GDPR-compliant processing, and 75% lower recurring OCR costs per year
Used technologies: Python, Docker, Microservices, FastAPI, PyTorch, Torchvision, MongoDB, MySQL
Yahya Sabi
Last position:
Odoo Software Developer & Consultant at KNAUER Wissenschaftliche Geräte GmbH
- Planning and implementing tailored Odoo ERP solutions to support and optimize specific business processes
- Configuring and customizing Odoo modules according to individual customer requirements, including process automation and data integration
- Training and supporting end users and administrators to ensure full use of Odoo features and to enhance user skills
- Providing ongoing post-implementation support, including troubleshooting, maintenance and updates to adapt to new business requirements
- Migrating data and integrating external applications into Odoo environments for a seamless, unified data landscape
- Analyzing and improving existing Odoo systems to boost efficiency and optimize the user experience
Nino Sandmeier
Last position:
Freelancer in Data Science at International Companies
Proceeding what was started in 10/2023, offering data science development skills fulltime to international clients
Helping companies learn more about their existing (unstructured) data, optimize processes and technical systems, and derive solutions for their problems
Tools and technology used: Python (sklearn, pandas, numpy, Django, sqlAlchemy, pyTorch), Matlab, Docker, AWS EC2, Lambda, S3, SQL, MySQL, Hadoop & Spark, Machine Learning, DNN, AI, Jira, Confluence, Git, CI/CD, GitLab, Jenkins
Daniel Suszczynski
Last position:
Engineering Leader & AI-Assisted Developer at Independent · Building with AI
- Building a full-stack e-commerce product using AI-assisted development, deliberately returning to hands-on engineering to validate how AI changes software development workflows and team dynamics.
- Exploring VP Technology, Head of Engineering, and Director of Engineering opportunities where hands-on AI experience meets organisational scaling expertise.
- Open to advisory conversations on AI-augmented engineering teams, technology strategy, platform architecture, and organizational design.
- No registered business. No commercial activity.
Jana Bettzüge
Last position:
Senior Business Analyst at Eurofiber Netz GmbH
- Responsible for cross-department requirements management in collaboration with business units, IT, and external vendors
- Gathering, structuring, consolidating, and prioritizing business requirements from various company departments
- Modeling and documenting business processes using BPMN as a basis for transparency and further development of existing solutions
- Analyzing existing business processes and identifying optimization opportunities
- Refining business requirements with regard to feasibility, cost-effectiveness, and impact on adjacent processes
- Facilitating business alignment sessions with relevant stakeholders to refine requirements and support decision-making
- Creating actionable business concepts for IT development
- Supporting implementation, testing, and rollout of new or adjusted solutions
- Assisting in standardizing requirements and processes to build a reliable decision-making foundation
Aamruth Shankar
Last position:
Head Product Manager at GoShippo
- Led 5 cross-functional teams (Product, Engineering, Data, Operations, and Design) across the transportation domain, covering carrier integration, shipment tracking, PUDO, reconciliation, and automation.
- Defined and executed the end-to-end product and integration strategy for 15+ global carriers across Europe and the U.S., ensuring unified APIs.
- Owned the carrier onboarding framework, reducing integration time by 40% through standardization, automation, and improved developer tooling.
- Partnered with major platform customers like Shopify and Wix to analyze EU merchant needs and enhance the shipping and fulfillment experience.
- Led the reconciliation automation initiative, reducing billing discrepancies by 30% and improving invoice validation speed for finance and operations teams.
- Collaborated with data analytics and operations teams to build carrier performance dashboards that improved on-time delivery rates and merchant satisfaction.
- Fostered a strong culture of cross-functional ownership, ensuring seamless collaboration between engineering, product, and external carrier partners.
Julien Look
Last position:
MLOps Engineer at SAMGEN
- Building and scaling cloud infrastructure on GCP to support a SaaS platform for industrial clients
- Designing and implementing a data-driven DevOps pipeline for streamlined deployment and CI/CD workflows
- Collaborating with Data Science team on MLOps workflow to automate integrated retraining
Discover over 15,000 top freelancers
Statistics of experts using Data Science
Aggregated from the professional profiles of matched freelancers.
Experience
14 years (Germany: 15 years)
Position duration
2.2 years
Positions per freelancer
8 (Germany: 9)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Education, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100% (Germany: 96%)
Master's degree or higher
77% (Germany: 78%)
Doctorate
20% (Germany: 22%)
Certifications per freelancer
2 (Germany: 3)
Most common languages
English, German, Spanish
Speak two or more languages
94% (Germany: 96%)
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Berlin are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Berlin using Data Science
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it covers
Data science turns raw data into usable insight. Teams use it to predict demand, spot risk, segment customers, and automate decisions. It often combines statistics, machine learning, and clear reporting so business and product teams can act on the results.
Typical work
- Exploratory data analysis and data cleaning
- Feature engineering and model training
- Forecasting, classification, and clustering
- Experiment analysis and KPI tracking
- Dashboards and data storytelling
Tool stack
Strong specialists usually work with Python, SQL, pandas, scikit-learn, Jupyter, and notebook-based workflows. Depending on the setup, they also use Spark, Airflow, dbt, cloud data warehouses, and version control to keep models and pipelines reproducible.
When to hire
Bring in freelance support when your team needs a faster path from data to decision, or when a project needs a specific skill set for a short period. This is common for prototype models, model reviews, audit work, backlog cleanup, and analytics support during a product launch or migration.
What strong specialists do
Good professionals do more than fit a model. They define the question, check data quality, explain trade-offs, and deliver results that others can use. In Berlin, that often means working with mixed teams in English, plus clear handover across product, analytics, and engineering.
Quality signals
Look for clear problem framing, sound methodology, and evidence that the specialist can work with messy real-world data. Good work is easy to review: assumptions are stated, results are reproducible, and the output fits the business context instead of just the notebook.
Frequently asked questions
What clients ask us most about Data Science — answered in short.
Data Science is used to turn business data into forecasts, classifications, segments, and measurable decisions. Companies rely on it for customer analysis, demand planning, risk detection, experimentation, and operational reporting. The best specialists connect the model output to a real business question, not just the dataset.
Data Science sits between analytics and machine learning. Analytics focuses more on reporting and trends, while machine learning focuses on building predictive models. Data science can include both, plus data cleaning, feature work, and interpretation.
A strong Data Science specialist usually has Python, SQL, statistics, and solid data cleaning habits. Depending on the project, they may also need pandas, scikit-learn, notebook workflows, visualization tools, and familiarity with cloud data stacks. Communication matters just as much as technical depth because the work has to be explainable.
For Data Science, you do not need a finished model spec, but you do need a clear problem, data sources, and a success definition. A good brief explains what decision the work should support, who will use it, and what systems or formats are involved. That helps the specialist choose the right approach quickly.
Data Science work is often done remotely because most tasks happen in notebooks, SQL tools, and shared documents. On-site time in Berlin can help when teams need deeper workshop sessions, stakeholder alignment, or access to sensitive internal data. Many projects work best with a mixed setup.
When hiring for Data Science, compare it with business intelligence, data engineering, and machine learning-focused work. If you mainly need dashboards and reporting, BI may be enough. If you need pipelines and storage, data engineering matters more; if you need a production prediction system, the machine learning side becomes more important.
Review how the Data Science professional explains data quality, assumptions, and limitations. Strong work is reproducible, uses appropriate validation, and produces outputs that are understandable to non-specialists. Ask for examples where the result changed a decision, not just where the model looked impressive.
Data Science often works best with adjacent expertise in data engineering, product analytics, experimentation, and domain knowledge. If the project goes into production, MLOps or software integration skills may also matter. The right mix depends on whether the goal is insight, prediction, or an operational workflow.
The average hourly rate of freelancers in Berlin, Germany who have used Data Science in their recent projects is 95 €, which corresponds to a daily rate of about 761 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Data Science in their recent projects, 100% hold at least a Bachelor's degree, 77% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Data Science in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 2.2 years.
The most common languages among freelancers in Berlin, Germany who have used Data Science in their recent projects are English (98%), German (96%), and Spanish (18%).
The most common industries among freelancers in Berlin, Germany who have used Data Science in their recent projects are Information Technology (82%), Education (53%), and Professional Services (49%).
The most common business areas among freelancers in Berlin, Germany who have used Data Science in their recent projects are Information Technology (82%), Product Development (80%), and Business Intelligence (65%).
Main locations of FRATCH Experts, who have recently used Data Science
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Hamburg 
Munich 
Cologne 
Frankfurt 
Stuttgart 
Dusseldorf 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
