Find the right Data Scientists in Berlin in minutes from over 15,000 CVs with the power of AI
Need 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.
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.
Meet FRATCH Data Scientists
Haseeb Zahid
Senior AI Engineer | LLM Engineer | ML Engineer
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.
Muzamal Ali
Data Scientist | AI Engineer
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.
Ashwin Parthasarathy
Data Scientist
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 Ali
Research Associate and Data Scientist
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 Mishra
Senior Analytics Professional
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.
Philipp Großer
Machine Learning Engineer
Last position:
Machine Learning Engineer at docmetric GmbH
- Analyzed patient data for various clients
- Developed complex analysis pipelines
- Performed quality assurance on methods
Meisam Ghafarlangroudi
Data Scientist
Last position:
Data Scientist at Geeks Ltd
Geeks Ltd is an educational technology company focused on innovative language learning solutions. They developed the WordUp mobile app, a leading platform for mastering English vocabulary through AI-driven personalized learning.
- Utilized a Large Language Model (LLM) at WordUp, tailored to enhance vocabulary learning by understanding and generating contextual examples, improving personalized learning experiences
- Utilized machine learning to analyze user behavior and predict churn, identifying key engagement trends that led to a 15% increase in user retention and satisfaction
- Designed and analyzed A/B experiments for new app features, measuring engagement and retention impact; provided actionable insights that improved user activation by 15%
- Developed a Customer Lifetime Value (CLTV) prediction model, leading to a 10% increase in average CLTV through targeted retention efforts
- Developed a Bayesian Marketing Mix Model to optimize budget allocation across digital and offline channels, improving ROI by 12%
- Designed and implemented a high-performance Python ETL pipeline, optimizing CPU and I/O utilization and streamlining data cleansing logic, resulting in a 30% reduction in processing time
- Reduced abandonment rate by 10% by analyzing user onboarding behavior and identifying key drop-off points
Discover over 15,000 top freelancers
Data Scientists statistics
Typical experience
9 years
Average project duration
2.5 years
Certifications per freelancer
2
Top business areas
Business Intelligence, Information Technology, Product Development
Top industries
Information Technology, Education, Food and Beverage
Most common languages
English, German, Malayalam
Bachelor's degree or higher
100%
Master's degree or higher
63%
Daily Rate Distribution
The chart shows how the daily rates of freelancers in this role 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. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Average rates for Data Scientists & Seniority distribution
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.
Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Frequently Asked Questions
Want to learn more? Find helpful information about FRATCH
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 60 €, which corresponds to a daily rate of about 479 € based on an 8-hour working day.
Of the freelancers working as Data Scientists in Berlin, 100% hold at least a Bachelor's degree and 63% hold at least a Master's degree.
On average, freelancers working as Data Scientists in Berlin have 9 years of professional experience, with a single engagement typically lasting around 2.5 years.
The most common languages among freelancers working as Data Scientists in Berlin are English (88%), German (75%), and Malayalam (13%).
The most common industries among freelancers working as Data Scientists in Berlin are Information Technology (88%), Education (50%), and Food and Beverage (38%).
The most common business areas among freelancers working as Data Scientists in Berlin are Business Intelligence (88%), Information Technology (88%), and Product Development (88%).
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Philipp Thomaschewski
FRATCH CEO
