pandas Expert in Berlin
for reliable data analysis, matched in minutes with vetted professionalsHire experts who clean and transform structured data, build repeatable analysis workflows, and connect pandas with Python, NumPy and Jupyter. FRATCH matches you quickly and precisely with vetted, available freelancers for your project.
Meet FRATCH Experts in Berlin, who have recently used pandas
Gilad G.
Last position:
European Strategy Atlas – Independent Analytics & Decision-Support Project at Independent Project
Designed and built an end-to-end interactive decision-support application using public European data across 27 EU countries and multiple strategic dimensions. Developed a structured analytical methodology for comparing countries, identifying patterns and trade-offs, and exploring strategic choices rather than presenting static dashboards. Translated complex multidimensional data into guided interactive exploration and learning workflows for non-specialist users. Built the application end-to-end using Python and Streamlit, with AI-assisted development and Git-based version control. Developed the project independently from problem framing and data analysis through methodology, UX logic, implementation and deployment.
Tools: Python, Streamlit, Git, AI-assisted development
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Mukund B.
Last position:
Voice AI Chatbot - Real-Time Audio Assistant
- ▶ Built real-time voice assistant (STT → LLM → TTS pipeline) benchmarking and evaluating multiple STT providers including faster-whisper and Azure Speech. achieved sub-3s latency, Groq API (Llama 3) with multi-turn memory - directly handling edge cases in dictation, names and passcode recognition.
Nisanthan S.
Last position:
Business Intelligence Consultant (freelance) at NBIC – Nisanthan BI Consulting
Advising companies on building, migrating and optimising BI and reporting landscapes (Power BI, SQL, Python, ETL)
5 client engagements in real estate and finance since 05/2025: taking over and stabilising existing reporting, automating recurring standard and management reports, building cash-flow models
Proposal and feasibility assessments for BI and reporting projects
Using AI-assisted development (Claude Code) to accelerate automation, tooling and web/app development
Custom ERP system
Problem: A client's core processes ran on scattered, siloed Excel files with no central data storage – error-prone, hard to scale and impossible to analyse end-to-end.
Approach: Captured the business processes and requirements, modelled the data and developed iteratively together with the business team.
Implementation: Built a tailored, web-based ERP system with a central database, role-based modules and automated reporting – delivered using AI-assisted development in Claude Code.
Timesheet app
Starting point: Time tracking based on an overgrown, macro-heavy Excel template – maintenance-intensive, single-user and error-prone.
Implementation: Migrated all functionality and VBA macros into a standalone web app with central data storage, multi-user support and automated reporting.
Cash-flow modelling
Starting point: The existing cash-flow model covered standing investments only; project developments were missing from steering.
Implementation: Built and extended the CF model to include project-development cash flows.
Optimisation: Reviewed and optimised existing CF models and expanded the KPI outputs for reporting and steering.
Murad H.
Last position:
Founder & Technical Lead at Hubpoint.Ai
- Founded an AI-powered scheduling and business-management SaaS for SMBs, owning technology strategy, architecture, product development, UX, billing and go-to-market execution.
- Architected and shipped a multi-tenant platform with REST APIs, RBAC, CRM, billing and notifications, powering the manager dashboard, admin console, booking experience and iOS/Android applications.
- Led and mentored 7 software engineers, 1 DevOps engineer, 1 QA engineer and 1 UX/UI designer, while remaining hands-on across backend, frontend and product delivery.
- Built AI voice and chat agents using Python/FastAPI, OpenAI and Anthropic APIs, RAG, pgvector and tool calling; integrated Twilio, Google Calendar/Meet, Stripe and Firebase.
- Owned production infrastructure and automated delivery across separate environments using Docker, Nginx, GitHub Actions and Grafana; represented the company at accelerators and international startup events.
Selected stack: Python, FastAPI, Node.js, Vue 3, React/Next.js, React Native, PostgreSQL, Redis, Docker
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.
Wolfram K.
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
Victor O.
Last position:
AI Training Engineer at Confidential AI Research Client
- Codebase Evaluation & Problem Design: Designed and stress-tested complex software engineering problems against large open-source Python codebases (including pandas), requiring deep context acquisition and architectural understanding to produce well-scoped, realistic problem statements aligned to strict correctness guidelines.
- Agent Failure Analysis: Assessed LLM coding agent solutions for correctness and completeness, identifying meaningful failures across edge case handling, dtype behaviour, and multi-column NaN propagation logic; documented findings with precision for downstream evaluation use.
- Programmatic Test Suite Development: Authored comprehensive pytest suites to programmatically verify agent-generated solutions against defined requirements, with deliberate coverage of boundary conditions and failure modes not caught by naive implementations.
- Containerised Environment Engineering: Built and debugged Docker environments for reproducible agent execution, including git-based repository provisioning, dependency pinning with npm ci, and multi-stage Dockerfile authoring across Linux-based containers.
Diogo S.
Last position:
Backend Engineer and AI Orchestrator at Stealth Startup
- Providing freelance software engineering and AI orchestration services for an early-stage startup.
- Designing and coordinating autonomous AI systems capable of executing complex, multi- step workflows.
- Developing customer-facing pilots and proof-of-concept solutions.
- Participating in meetings with customers and investors to support product development and business discussions.
Enrico G.
Last position:
Freelance Software & Data/AI Engineer at Freiberuflicher Software & Data/AI Engineer
- Lecturer for the GenAI Track at the Master School Institute of Technology
- Development of a full-stack AI application (React + Python/FastAPI) for automated supplier product import with intelligent column and category classification (4-layer hierarchical) including human-in-the-loop validation
Ibrahim H.
Last position:
Senior Full Stack / AI Engineer at Punktum Digital GmbH
- Context: Healthcare and laboratory teams required faster document analysis, treatment-planning support, and reliable AI workflows for MR/VR-assisted operations.
- Contribution: Built the AI healthcare platform, model/agent workflows, VR-glasses deployment platform, REST APIs, Next.js/React interfaces, and CI/CD pipelines.
- Impact: Delivered a production-ready AI product foundation that improved clinical document review, supported laboratory automation, and made VR fleet deployment manageable across environments.
Tech: TypeScript, Next.js, Node.js, React, Java, Spring Boot, Python, PyTorch, TensorFlow, Docker, PostgreSQL, OpenAPI, GitLab, GitHub Actions.
Mathias W.
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
Santina W.
Last position:
Business Analyst & BI Strategist - Comparison Portal at dataweys (self-employed)
- Assessment of the existing reporting landscape and strategic bundling of needs
- Migration and consolidation of reports to Metabase, connected to ClickHouse as the data foundation
- Building and maintaining data pipelines
Stack: Metabase · ClickHouse · Appsmith · Airflow
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
Louis G.
Last position:
Freelance Solutions Architect and Machine Learning Engineer at Self-employed
- Develop and demonstrate solutions using GenAI software like langchain, vercel ai sdk, copilotkit
- Work with customers to understand their challenges and provide the best solutions based on open-source data products
- Build RAG and GraphRAG solutions using Neo4j, lancedb, and Postgres
- Deploy a LLMOps platform using kubernetes, terraform, helmfile, Arize phoenix, mlflow
- Architect and build data pipelines using dbt, Trino, Spark, Iceberg, Airflow, ArgoCD, terraform, kubernetes
- Delivered user-centred technical strategy for Agriculture 4.0 and precision livestock farming, helping my client secure funding from Bpifrance
- Delivered a prospecting tool for a leading French solar carport installer, using geospatial computing (GIS), speeding up the sales process
- Built digital twin architecture for solar carports and EV chargers, making real-time monitoring and smart charging possible
Qaiser A.
Last position:
Freelance Lead DevOps Engineer at Schwarz Gruppe Produktion
Bootstrapping a CloudOps team and building a multi-cloud provider backend for a low-code Internal Developer Platform (IDP) with env zero
Introducing user story mapping, ADRs, milestones, and backlog management
Designing and developing core APIs, setting up CI/CD pipelines, OpenTofu/Terraform scripts
Representing and communicating the team with third-party stakeholders (e.g. env zero)
(Cross-)team coaching on DevOps, software design, Terraform, Golang, and agile practices
Discover over 15,000 top freelancers
Statistics of experts using pandas
Aggregated from the professional profiles of matched freelancers.
Experience
11 years (Germany: 12 years)
Position duration
2 years (Germany: 2.7 years)
Positions per freelancer
7 (Germany: 8)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Education, Healthcare
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
98%
Master's degree or higher
81% (Germany: 80%)
Doctorate
19% (Germany: 17%)
Certifications per freelancer
2
Most common languages
German, English, Arabic
Speak two or more languages
97% (Germany: 99%)
Based on our profile pool as of 9 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology 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 of experts in Berlin using pandas
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 9 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
pandas 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 (82%)
- Education (46%)
- Healthcare (36%)
- Professional Services (34%)
- Banking and Finance (28%)
- Automotive (23%)
- Retail (23%)
- Government and Administration (20%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Data analysis
pandas is an open-source Python library for working with structured data. Its DataFrame and Series objects help teams load, inspect, clean, transform and analyse information from files, databases and APIs. It is widely used for exploratory analysis, reporting, forecasting preparation and data quality work.
Core capabilities
Professionals use pandas to turn raw datasets into clear, repeatable results. They handle missing values, inconsistent formats, duplicate records, joins, filters, grouping and time-based analysis while keeping transformations understandable and testable.
- Import CSV, Excel, JSON and database data
- Reshape, merge and aggregate DataFrames
- Prepare datasets for modelling and reporting
- Validate outputs and document data assumptions
Python ecosystem
pandas works closely with NumPy for numerical operations, Jupyter for interactive analysis and Matplotlib or Plotly for visualisation. Strong specialists also connect it with SQL, SQLAlchemy, scikit-learn, PyArrow and cloud storage, choosing the right format and processing approach for each workload.
Project use cases
Companies bring in pandas expertise for customer analysis, financial reporting, operational dashboards, experimentation and migration from spreadsheet-based processes. It can support a quick investigation or form a well-structured data preparation layer within a larger Python service.
- Consolidate data from business systems
- Automate recurring reports and quality checks
- Prepare features for machine learning
- Investigate trends, anomalies and business questions
When to hire
Freelance support is useful when internal teams need a clean analysis quickly, a pipeline has become difficult to maintain, or a prototype must become a reliable workflow. Berlin companies can work with specialists remotely or on site, depending on access requirements, stakeholder workshops and the need for German- or English-language collaboration.
Quality signals
A strong pandas professional separates exploration from production code and makes data lineage visible. Look for clear handling of edge cases, sensible memory use, meaningful tests, documented assumptions and outputs that another specialist can reproduce. Experience with SQL, version control, packaging and orchestration adds value when notebooks must become dependable processes.
Frequently asked questions
Everything clients usually want to know about pandas, in one place.
pandas is used to load, clean, combine and analyse structured data in Python. Companies often use it for reporting, data preparation, quality checks, exploratory analysis and creating inputs for machine learning.
pandas provides programmable, repeatable transformations that are easier to version and test than spreadsheet workflows. SQL is usually stronger for filtering and aggregating data inside a database, while pandas is well suited to in-memory analysis and combining data from different sources.
A strong pandas specialist often works with Python, NumPy, SQL and Jupyter. Depending on the project, useful adjacent skills include data visualisation, scikit-learn, cloud storage, PyArrow, testing and workflow orchestration.
A focused analysis may suit a professional who can work confidently with DataFrames, joins, missing values and clear documentation. A production workflow needs broader experience with testing, performance, data contracts, deployment and the systems that supply and consume the data.
pandas can handle substantial datasets when the workflow is designed around memory use, efficient data types, suitable file formats and selective loading. For data that exceeds a single machine's practical limits, a specialist may combine pandas with SQL, Dask, Spark or database-side processing.
Yes, much pandas work can be completed remotely because the main deliverables are code, notebooks, tests and documented results. On-site sessions in Berlin can still help when specialists need direct access to internal systems, workshops with stakeholders or close coordination with a local data team.
Ask how the professional validates inputs, handles missing and unexpected values, tests transformations and documents assumptions. Good pandas work is reproducible, readable and efficient, with outputs that can be checked independently rather than relying only on a polished notebook.
pandas notebooks can be a useful starting point, but production use usually requires separating reusable logic from exploration. A capable specialist can add tests, configuration, logging, dependency management and orchestration so the workflow runs consistently outside the notebook.
The average hourly rate of freelancers in Berlin, Germany who have used pandas in their recent projects is 84 €, which corresponds to a daily rate of about 674 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used pandas in their recent projects, 98% hold at least a Bachelor's degree, 81% hold at least a Master's degree, and 19% hold a doctorate.
On average, freelancers in Berlin, Germany who have used pandas in their recent projects have 11 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers in Berlin, Germany who have used pandas in their recent projects are German (98%), English (98%), and Arabic (10%).
The most common industries among freelancers in Berlin, Germany who have used pandas in their recent projects are Information Technology (82%), Education (46%), and Healthcare (36%).
The most common business areas among freelancers in Berlin, Germany who have used pandas in their recent projects are Information Technology (90%), Product Development (74%), and Business Intelligence (67%).
Main locations of FRATCH Experts, who have recently used pandas
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.
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Munich 
Cologne 
Frankfurt 
Stuttgart 
Dusseldorf 
Essen 
Nuremberg Request a free demo
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FRATCH CEO
