Data Scientists in Berlin
in minutes from over 15,000 CVs with the power of AINeed support for machine learning models, statistical analysis, Python and SQL pipelines, or product analytics? Work with freelance data scientists who can turn messy data into decisions, predictions, and clear deliverables. Fast, precise matching with vetted, available freelancers.
Meet FRATCH Data Scientists in Berlin
Nikolai G.
Last position:
Clinical Data Manager at Dr. Falk Pharma
- Used OpenCode and AI-assisted software engineering to design, implement, refactor, test, and document an end-to-end RAW/SDTM/ADaM pipeline in R for Dr. Falk Pharma (07/2026), including metadata-driven transformations, automated validation rules and QC, traceability, and reproducible clinical outputs.
Haseeb Z.
Last position:
Senior Data Scientist at WPP MEDIA
- Designed and deployed enterprise Retrieval-Augmented Generation (RAG) applications using LangChain, LangGraph, vector databases, embeddings, and open-source LLMs served through vLLM on GCP GPU infrastructure.
- Built agentic AI workflows using LangGraph with planning, reasoning, tool execution, persistent memory, session management, and Human-in-the-Loop approval mechanisms.
- Developed LLM-powered automation systems integrating BigQuery, SQL pipelines, and external advertising APIs including Meta, TikTok, Amazon, Snapchat, Google, and Pinterest, reducing manual operational workflows.
- Architected multi-agent AI systems for enterprise analytics and decision-support workflows, enabling autonomous task execution and intelligent data interactions.
- Implemented retrieval optimization strategies including multi-retriever architectures, semantic search, context optimization, and query improvement techniques, improving response relevance by approximately 40%.
- Engineered structured prompting strategies, function-calling schemas, and validation workflows to improve reliability of multi-step LLM applications.
- Designed scalable AI services using Python, FastAPI, Cloud Run, Pub/Sub, BigQuery, Docker, and cloud-native deployment architectures.
Uditha W.
Last position:
Data Science Tutor at University of Europe for Applied Sciences
- Taught Python, Pandas, data analysis, visualization and Power BI to 250+ students.
- Guided practical projects from data preparation and exploratory analysis through visualization and dashboard creation.
- Explained complex analytical concepts clearly to audiences with different levels of technical experience.
Muzamal A.
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.
Nino S.
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
Ashwin P.
Last position:
Data Scientist at Mercor Intelligence
- Elevated LLM output reliability by engineering domain-specific prompts and evaluation logic, improving reasoning consistency across production language model workflows.
- Designed advanced coding benchmarks and validated solutions to strengthen training and evaluation datasets, improving model performance on technical problem-solving tasks.
- Designed and implemented automated evaluation frameworks for technical reasoning tasks; optimized LLM output reliability by 15% through rigorous prompt engineering and rubric-based benchmarking.
Sara A.
Last position:
Research Associate and Data Scientist at National Center of Robotics and Automation - Condition Monitoring Lab
- Developed ASR and TSR-based speech processing pipelines on AWS, enabling efficient feature extraction and scalable deployment for speech and text analytics.
- Built a Multimodal Speech Emotion Recognition system combining NLP and deep learning (audio + text), achieving 98% accuracy and supporting real-time, cloud-based inference.
- Designed and optimized end-to-end model training and evaluation workflows using AWS services (S3, EC2, Lambda) to ensure performance, reliability, and reproducibility.
- Created and deployed interactive, user-friendly dashboards for data visualization and insight generation, supporting research teams and management in data-driven decision-making.
Sanu M.
Last position:
Decision Scientist III at Vinted GmbH
Built an FRT (Full Resolution Time) data product in dbt and BigQuery with a MECE ticket lifecycle methodology derived from a unified semantic mapping and ordered event stream.
Delivered reusable macros, modular models, automated unit tests, and a LookML metric layer adopted by Process Improvements and Ops.
Overhauled FRT experiments using quasi-experimental and pre-post causal analyses to demonstrate that slower resolution affected GMV, enabling shifting from a blanket 70%-in-48h SLA to problem-specific targets and providing the analytical foundation for SLA redesign.
Robin S.
Last position:
Consultant, Data Science & Engineering at valantic Digital Finance GmbH
- Bridged business and engineering for enterprise finance clients, designing data products and cloud pipelines in Python, SQL Server, SAP Datasphere, and Tagetik
- Conceived, built, and containerised a Python/FastAPI universal connector that syncs SAP S/4HANA and other SQL/NoSQL sources to Tagetik, deployed on Google Cloud Run and Microsoft Azure, cutting a critical 90-minute data load to approximately 80 seconds (65× faster)
- Architected a medallion-layer SQL Server warehouse ingesting approximately 500 GB/day from 11 ERP instances, automating daily refreshes (full load under 6 minutes) and freeing 20–30 finance staff from days of manual data consolidation
- Led cross-functional workshops to design enterprise EPM target architecture for a leading Southeast-Asian telecom (CAPEX, OPEX, revenue), translating requirements into data-model specifications and integration blueprints now being built by the client’s implementation team
- Delivered selected projects including a consolidated data & reporting warehouse for a global manufacturer (10 k+ employees), NFI reporting for an international management & technology consultancy, and CAPEX/OPEX planning for a Southeast-Asian telecom (20 k+ employees)
Meisam G.
Last position:
Senior AI Engineer / Data Scientist at Geeks Ltd (WordUp)
Geeks Ltd is a UK-based technology company; WordUp is its AI-driven language-learning product focused on personalized vocabulary learning and intelligent educational experiences.
- Coordinate AI product delivery across Product, Engineering, Data, Operations, and leadership, translating user needs into scoped initiatives, sequencing work, surfacing blockers, facilitating hand-offs, and communicating progress.
- Own search, recommendation, retrieval, and content-enrichment features end to end, from requirements and architecture through Python/FastAPI implementation, testing, deployment, monitoring, and rapid iteration.
- Developed low-latency retrieval, ranking, and personalization services using AWS, OpenSearch, DynamoDB, embeddings, and reusable APIs, achieving <1s latency, 22% higher engagement, and 12% higher premium conversion.
- Use AI coding assistants for codebase analysis, scaffolding, refactoring, tests, debugging, and documentation while reviewing every output for correctness, architectural fit, security, maintainability, and user value.
- Represent technical work in planning and stakeholder discussions, gather requirements first-hand, challenge priorities constructively, explain delivery trade-offs, and help teammates make outcome-focused decisions.
Philipp G.
Last position:
Machine Learning Engineer at docmetric GmbH
- Analyzed patient data for various clients
- Developed complex analysis pipelines
- Performed quality assurance on methods
Mark W.
Last position:
IT Manager at WESSLING Consulting Engineering
- Overall responsibility for IT strategy and IT operations, reporting directly to the executive management.
- Implementation of business requirements as production-ready solutions within a few days, supported by practical data science experience and expertise from the energy sector.
- Led the IT carve-out of a medium-sized company: separation of infrastructure, systems, and contracts into an independent IT organization.
- End-to-end responsibility for IT infrastructure: hardware procurement, cloud services (including GPU resources), and networking.
- Selected and introduced a new ERP system together with the executive management.
- Managed external MSPs and service providers, including contract, SLA, and license management.
- Responsible for IT security, GDPR compliance, backup, and business continuity; provided IT support for ISO 9001 certification.
- IT budget planning and cost optimization.
Joseph Chris Adrian R.
Last position:
Data Scientist II at Amazon
- Engaged stakeholders to understand requirements and define the project scope and success criteria
- Demonstrated adaptability by quickly ramping up in a complex, ambiguous regulatory space
- Authored comprehensive science design, architecture review and final methodology documentation ensuring reproducibility
- Gathered data stored in Amazon Redshift and Amazon S3 using SQL
- Performed exploratory data analysis and feature engineering using Python (matplotlib and seaborn), PySpark and Amazon EMR
- Developed and validated machine learning models to facilitate optimization, time-series forecasting, anomaly detection and classification
- Developed machine learning models using Python libraries such as scikit-learn, numpy and pandas
- Deployed the machine learning model using AWS cloud platform (MLOps), especially AWS SageMaker
Discover over 15,000 top freelancers
Data Scientists statistics
Aggregated from the professional profiles of matched freelancers.
Experience
9 years (Germany: 13 years)
Position duration
2.3 years (Germany: 2 years)
Positions per freelancer
6 (Germany: 8)
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Education, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
100% (Germany: 99%)
Master's degree or higher
69% (Germany: 83%)
Doctorate
15% (Germany: 35%)
Certifications per freelancer
1 (Germany: 4)
Most common languages
English, German, Bulgarian
Speak two or more languages
77% (Germany: 94%)
Based on our profile pool as of 6 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this role in Berlin are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates for Data Scientists in Berlin
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 6 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Data Scientists experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (92%)
- Education (54%)
- Professional Services (54%)
- Banking and Finance (31%)
- Automotive (23%)
- Energy (23%)
- Food and Beverage (23%)
- Healthcare (23%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the role
What they do
Data scientists turn raw data into answers, models, and decisions. They work on forecasting, classification, segmentation, experimentation, and analytics that support product, marketing, finance, or operations teams. In many projects, they also define the question, clean the data, and explain what the results mean for the business.
Typical deliverables
- Exploratory analysis and data quality checks
- Predictive models and baseline comparisons
- Experiment design and result analysis
- Feature sets and reusable notebooks
- Dashboards, reports, and clear stakeholder summaries
- Deployment support for model handover to engineering or MLOps
Core skills
A strong data scientist combines statistics, coding, and business judgment. Python and SQL are standard. So are pandas, scikit-learn, NumPy, and often notebook-based work in Jupyter or Databricks. Depending on the project, they may also use TensorFlow, PyTorch, Spark, dbt, or cloud tools in AWS, Azure, or GCP.
What matters most is not just model building. It is choosing the right method, testing assumptions, avoiding leakage, and making results understandable for non-technical teams.
When companies bring one in
Freelance data scientists are useful when a team needs focused help for a specific problem, such as churn prediction, demand forecasting, pricing, recommendation logic, or KPI analysis. They also help when internal teams lack time for a deep dive, or when a project needs an outside view before a bigger data initiative starts.
In Berlin, this often fits startups, scale-ups, agencies, and product teams that want flexible support without a long hiring process. Remote work is common, but on-site workshops can help at the start of a project or when data access is sensitive.
What strong experts bring
- A clear method for turning business questions into testable hypotheses
- Clean, reproducible analysis and well-structured code
- Careful treatment of missing data, bias, and model validation
- Strong communication with product, engineering, and business teams
- A focus on impact, not just technical accuracy
The best data scientists can move between discovery work, model building, and stakeholder communication without losing the thread. They know when a simple model is enough and when a more advanced approach is justified.
Adjacent titles
Searches for this role often include data analyst, machine learning engineer, or ML specialist. The overlap is real, but the focus differs. A data analyst usually emphasizes reporting and descriptive insights. A machine learning engineer focuses more on production systems. A freelance data scientist sits between both when the project needs analysis, modeling, and practical delivery in one role.
Frequently asked questions
What clients ask us most about Data Scientists — answered in short.
A Data Scientist usually takes a problem from question to insight or model. That can include data cleaning, exploratory analysis, feature work, experiment analysis, forecasting, classification, and stakeholder-ready reporting. In many projects, they also help define what should be measured before any model work starts.
A Data Scientist usually goes further into prediction, segmentation, testing, and model evaluation. A data analyst is more focused on reporting, dashboards, and descriptive trends. The roles overlap, but the scientist is typically brought in when the team needs statistical depth or machine learning support.
A freelancer makes sense when the work is project-based, urgent, or tied to one clear outcome. A Data Scientist is often hired this way for model development, pricing work, churn analysis, or a short diagnostic phase. It is also useful when you need specialist input before deciding whether to build a longer-term internal role.
A strong Data Scientist should be comfortable with Python, SQL, statistics, and data cleaning. They should also understand model validation, experiment design, and how to explain trade-offs clearly. Depending on the project, experience with cloud data stacks or machine learning libraries can matter a lot.
Yes, most data scientists can work remotely if the data access and communication setup is clear. For Berlin teams, a hybrid setup can help at the start of a project, especially for workshops with product, analytics, or engineering stakeholders. The best setup depends on data sensitivity and how closely the work must connect to internal systems.
A data scientist often works with Python, SQL, Jupyter, pandas, scikit-learn, and notebook-based analysis. For larger or more production-oriented work, Spark, Databricks, cloud data tools, and machine learning frameworks may be part of the stack. The method should fit the question, not the other way around.
Look for clear problem framing, clean code, and careful validation. A good Data Scientist can explain why a method was chosen, what the limits are, and how the result should be used. Strong work is practical, reproducible, and easy for other teams to take over.
In Berlin, data scientists are often hired by startups, e-commerce teams, digital products, mobility companies, and agencies with complex data questions. The work may cover customer behavior, recommendation logic, operations planning, or product experimentation. In every case, the main need is the same: turn data into decisions that teams can act on.
The average hourly rate for Data Scientists in Berlin is 69 €, which corresponds to a daily rate of about 552 € based on an 8-hour working day.
Of the freelancers working as Data Scientists in Berlin, 100% hold at least a Bachelor's degree, 69% hold at least a Master's degree, and 15% hold a doctorate.
On average, freelancers working as Data Scientists in Berlin have 9 years of professional experience, with a single engagement typically lasting around 2.3 years.
The most common languages among freelancers working as Data Scientists in Berlin are English (92%), German (85%), and Bulgarian (8%).
The most common industries among freelancers working as Data Scientists in Berlin are Information Technology (92%), Education (54%), and Professional Services (54%).
The most common business areas among freelancers working as Data Scientists in Berlin are Information Technology (100%), Business Intelligence (92%), and Product Development (85%).
FRATCH Data Scientists main locations
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.
Berlin 
Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
Munich Request a free demo
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Philipp Thomaschewski
FRATCH CEO
