pandas Expert
to turn complex data into clear decisions, matched in minutes with the power of AIHire experts who clean and reshape data, build reliable analysis workflows, and connect pandas with NumPy, SQL, Jupyter, and machine learning tools. FRATCH matches you quickly and precisely with vetted, available freelancers who fit your project.
Meet FRATCH Experts who have recently used pandas
Gabin Maxime N.
Last position:
Multi-Agent R&D Pipeline (3 Custom Agents) at Independent Project
Claude Code subagents, MCP, Pydantic V2, pytest, bandit
Designed and shipped 3 specialized agents that hand work down a line: a research agent writes a cited implementation spec, a coding agent builds the modular code and its tests, a review agent ranks findings by severity and applies the fixes. Each handoff is a structured document, so no stage depends on another agent's context window.
Connected the research agent to an academic-research MCP server (Semantic Scholar, ArXiv, Hugging Face Hub, citation snowballing) so every reference traces to a tool result rather than the model. Gated commits behind ruff, mypy, pytest and bandit, required human sign-off before installs and commits, and persisted session state on disk so long runs survive a context reset.
Patrick L.
Last position:
Senior GenAI Fullstack Developer at SBH (Schulbau Hamburg)
Remote freelance role focused on Agentic AI strategy, secure application patterns, and reusable agentic workflows for a government agency.
- Development and implementation of an open-source Agentic AI strategy for a government agency, with a focus on GDPR, security, and self-hosted solutions
- Development of reusable agentic workflows and business applications that enable non-technical employees to solve business problems independently
- Implementation of nine business applications with Single Sign-On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
Techstack: Python, Streamlit, Anthropic SDK (Claude), Azure, Linux, PostgreSQL, MS SQL, Angular
Fadi S.
Last position:
Development of a production-ready Enterprise Document AI & Recommendation Platform at Freelancer
- Development of a production-ready Enterprise AI solution for the automated processing of invoices and business documents
- Integration of Azure AI Document Intelligence and LLM technologies into existing business processes
- Development of robust REST APIs for automated document processing and system integration
- Extraction, validation, and storage of structured invoice data in Azure SQL as a base for analytics and machine learning models
- Development of an AI-based recommendation engine with machine learning and deep learning to generate personalized product recommendations based on historical purchase data
- Implementation of logging, monitoring, error handling, and validation mechanisms for stable production use
- Collaboration with business teams to define business rules and integrate the solution into existing enterprise processes
Technologies: Python, Azure AI Document Intelligence, Azure OpenAI, Azure SQL Database, REST APIs, Machine Learning, Deep Learning, OCR, Pandas, JSON, Workflow Automation
Matthias S.
Last position:
Technology Lead & Co-Founder at LegalMind GmbH
- Redesign of legal operations: standardised workflows reducing routine effort by up to 80%, with source citation, hallucination check as quality gate, role model, logging and audit trail.
- Compliance-by-design operating model (EU AI Act readiness, GDPR, eIDAS) with documented, releasable process steps.
- Roadmap, sprint planning and release management for an agentic RAG platform with counsel-in-the-loop approval, audit trail and German hosting.
- EU AI Act readiness, GDPR and eIDAS requirements managed as first-class project deliverables; go-to-market for two customer verticals.
Michael N.
Last position:
Senior AI Engineer | Forward Deployed Engineer at Tiefbau
- Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
- Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
- Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.
Mirza K.
Last position:
Agentic Automation and a RAG system
- This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.
Used: Python, RAG, LangGraph, LangChain, deepeval, MCP
Ramazan C.
Last position:
Fullstack-/DevOps Engineer at BKA (Federal Criminal Police Office)
Development and further development of an internal platform for managing and providing technical resources, virtual machines, and infrastructure services. The platform supports self-service processes and covers functions that are conceptually comparable to cloud management solutions like Azure or AWS.
- Responsible involvement in the design, development, and implementation of new backend and frontend features
- Hands-on development with Java, Spring Boot, Python, and Angular
- Implementation of REST interfaces, business logic, validations, and integrations into existing system landscapes
- Further development of modern web interfaces with Angular, including connection to backend services
- Participation in architecture and design decisions within the team, especially with regard to scalability, maintainability, and clean interfaces
- Containerization and deployment of applications with Docker, Kubernetes, and Helm
- Support with CI/CD processes and deployment to Kubernetes-based environments
- Work in the environment of vSphere, Broadcom, GitLab CI/CD, ArgoCD, Maven, npm, and NuGet
- Close collaboration with developers, business teams, DevOps, and other technical stakeholders
- Analysis of technical requirements, deriving suitable solutions, and independent implementation in an agile team
- Use of GitHub Copilot to support code generation, refactoring, test case creation, and technical documentation
Methods/ tools/ technologies: Languages & frameworks: Java (21), Spring Boot (4.x), Python, Angular, Robot Framework, Kubernetes, Helm Persistence: PostgreSQL, MongoDB, Hibernate, Liquibase Architecture & communication: REST, gRPC, GraphQL, Apache Kafka, OpenAPI, Microservices, Event Driven, Domain Driven Design Cloud & infrastructure: Terraform, Docker, Rancher, Helm, Ansible Security: OAuth2, MS (Entra ID), web security, Keycloak (extensions for detailed group rights) DevOps: GitLab CI/CD, Ansible, Maven, Gradle, Grafana, Prometheus, Git, GitHub Copilot Testing & QM: JUnit, Robot Framework, automated component and integration tests, E2E tests with Playwright, Testcontainers, EasyMock Methodology & approach: Kanban, JIRA, Confluence, Clean Code
Karin A.
Last position:
AI Benchmark Engineer | Native language specialist German at Lilt
- Task Engineering: Evaluating Coding Agents.
- Asset Creation: Building realistic task environments using datasets and files in German. Crucially, these assets must remain in the target language to genuinely measure multilingual handling.
- Prompting & Translation: finding failure points where AI does not work, in German.
- Implementation & Verification: Supporting the development of robust solutions (reference implementations) and write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary).
- Calibration & Execution: Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Opus).
- Quality Assurance: Participation in a rigorous, 4-layer human quality control process (creation, human review, calibration review, and audit) alongside automated LLM-based checks to ensure fairness, grammatical accuracy, and benchmark integrity.
- Linguistic Review: Reviewing AI benchmark tasks across Hindi, Arabic, Japanese, Chinese, Czech and Turkish.
Christine T.
Last position:
Communications Consulting at Storytrend
Most mid-sized companies already have their numbers. What is missing is the translation: a dashboard with forty tiles does not answer a single question that is actually asked in management.
Analysis
- Evaluation of existing data with Python and SQL
- Checking data quality and methodology before making a statement
- The result is an analysis that leads toward a concrete decision
Preparation
- Reports in Power BI and Tableau
- Interactive calculators and visualizations on the web
- Presentations and specialist texts for customers, sales and the public
- Analysis and communication from one source — that
Matthias S.
Last position:
Software Developer and Consultant at CLADE GmbH
- Analysis of the existing CAN communication between microcontrollers
- Analysis of the sensors used and the measured values collected
- Planning the CAN messages for transmitting the measured values
- Iterative adjustment of the microcontroller code to the new CAN messages
- Cross-compilation from x64 to arm64
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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Shanna T.
Last position:
Freelance Data Scientist & AI Developer at tellaev.de
- Portfolio development & customer acquisition
- Portfolio development (RAG, NLP fine-tuning, process automation with n8n) and active customer acquisition
- Positioning: GDPR-compliant, locally hosted AI solutions for SMEs
Daryoosh D.
Last position:
FP&A Data & AI Architect at Epta Group
Scope: Embedded as FP&A Data & AI Architect within the Finance function of a major European refrigeration manufacturer, leading the transformation of manual, fragmented financial reporting into an automated, governance-driven intelligence platform. Driving the shift from Excel-based controlling to structured data architecture, Power BI analytics, and AI-assisted financial operations.
Financial Data Integrity & ERP Governance
- Initiated and led GL vs. subledger reconciliation investigations, identifying and resolving structural mismatches between General Ledger and subledger data that had gone undetected prior to engagement
- Conducted asset analysis to identify items missing from General Ledger postings, surfacing gaps in fixed asset tracking and period-end completeness
- Validated SAP reports, establishing baseline data quality standards for Finance team consumption
- Established systematic SAP data validation framework ensuring ongoing integrity between ERP postings and downstream reporting outputs
Finance Reporting Transformation
- Designed and implemented a structured Transformation Project approach for converting manual Finance reports into fully automated processes
- Created and owns the Data Reporting Audit Log; a centralized tracking system capturing report owners, stakeholders, data sources, manual effort estimates, and automation opportunity scores across the Finance function
- Mapped the full reporting landscape identifying quick-win automation targets and strategic Power BI migration candidates
- Actively reducing manual Excel and PowerPoint dependency across FP&A workflows; replacing point-in-time snapshots with live, governed data models
Power BI & Analytics Enablement
- Introduced and presented Power BI as the strategic reporting platform to Finance leadership, building internal buy-in for the BI transformation roadmap
- Designed initial Power BI architecture aligned with SAP, Salesforce and Oracle data structures and FP&A reporting requirements
- Established report ownership, governance documentation, and data lineage standards enabling sustainable self-service analytics across the Finance team
Transformation Infrastructure & Collaboration
- Configured and deployed Jira as the transformation project management hub, establishing structured sprint workflows, backlog management, and progress visibility for Finance IT initiatives
- Proposed and initiated a dedicated FP&A Communication & Transformation Hub, a structured cross-functional forum aligning Finance, IT, and business stakeholders around the reporting transformation roadmap
- Positioned the Finance function as an active driver of data governance and digital transformation within the broader organization
Outcomes
- GL/subledger reconciliation gaps identified and investigation framework established within first two weeks of engagement
- Data Reporting Audit Log deployed; first structured inventory of Finance reporting landscape in company history
- Power BI transformation roadmap presented and approved by Finance leadership
- Jira-based project governance live; Finance transformation now tracked with full sprint visibility
Technologies: SAP FI/CO · Power BI · DAX · SQL · Excel (advanced) · Power Query (M) · Power Automate · VBA · Jira · Microsoft 365 · SharePoint · Salesforce (Sales Data) · Oracle HCM · Python
Yasin Y.
Last position:
Enterprise Architect at Bundesagentur für Arbeit
Task:
- Design and build a proof of concept (PoC) for a future-proof virtualization platform, taking secure system architectures into account
- Assess the current state of existing infrastructures and develop selection and evaluation criteria for the right OS virtualization platform
- Carry out the requirements analysis and then create and prioritize tickets in the ticket system
- Complete and continuously update a tool evaluation matrix based on PoC results
- Support team knowledge building through clear documentation of the approach and results in Confluence
- Enterprise analysis of existing hardware (creating different BoMs)
Technologies: Vmware, Vmware Aria Operations, Osism, Canonical OpenStack, FishOs, Linux, Terraform, Ansible, Confluence, Alma
Philipp G.
Last position:
Data Scientist & ML Engineer at Data-Science Factory GmbH
- Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
- Implementation of automated end-to-end cloud processes
- Development of LLM and NLP models
- Creation of interactive reports
- Support for national and international large corporations as well as medium-sized companies in implementing ML projects
Discover over 15,000 top freelancers
Statistics of experts using pandas
Aggregated from the professional profiles of matched freelancers.
Experience
12 years
Position duration
2.7 years
Positions per freelancer
8
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Education, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
98%
Master's degree or higher
80%
Doctorate
17%
Certifications per freelancer
2
Most common languages
English, German, French
Speak two or more languages
99%
Based on our profile pool as of 26 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology 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 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 26 Sep 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 (77%)
- Education (49%)
- Banking and Finance (32%)
- Automotive (31%)
- Healthcare (30%)
- Manufacturing (29%)
- Professional Services (28%)
- Retail (25%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Data analysis with pandas
pandas is an open-source Python library for working with structured and time-series data. Its DataFrame and Series objects make it practical to load, inspect, clean, transform, join, and analyze information from many sources. Companies use it to turn raw records into dependable datasets and repeatable insights.
Typical applications
pandas appears in analytics, reporting, forecasting, experimentation, and data preparation for machine learning. It supports work across finance, commerce, healthcare, manufacturing, logistics, and research.
- Combine CSV, Excel, JSON, database, and API data
- Clean missing, duplicated, inconsistent, or incorrectly typed values
- Prepare features and datasets for statistical or machine learning workflows
- Produce recurring reports, summaries, and exploratory analyses
Ecosystem and tooling
Strong pandas specialists usually work across the wider Python data ecosystem. They combine pandas with NumPy for numerical operations, Jupyter for investigation, Matplotlib or Seaborn for visualization, and scikit-learn for modeling. SQL, cloud storage, PyArrow, and workflow orchestration often matter when analysis moves into production.
When expertise matters
Companies bring in freelance pandas expertise when a data workflow has become slow, fragile, or difficult to explain. A specialist can standardize ingestion, clarify business rules, improve transformations, and leave behind tests and documentation that internal teams can maintain.
- Multiple sources produce conflicting formats or definitions
- Manual spreadsheet work delays reporting
- Analysis notebooks need to become repeatable pipelines
- Large datasets expose memory or performance problems
What strong specialists deliver
A capable professional understands both the data and the decisions it supports. They choose clear operations, validate assumptions, handle edge cases, and preserve data lineage. They also know when to push work into SQL, use vectorized operations, change a data type, or select a more suitable tool.
Quality shows in readable code, meaningful tests, documented inputs and outputs, and results that can be reproduced by another person. Strong communication matters as much as syntax when definitions are disputed or source data is incomplete.
Collaboration and handover
pandas work can usually be delivered remotely when access, sample data, and business context are available. Effective collaboration includes agreed data contracts, secure transfer methods, reviewable notebooks or modules, and clear acceptance criteria. On-site work can help when specialists must map undocumented processes or work closely with domain teams.
Before engaging a freelancer, define the source systems, expected outputs, refresh needs, data quality risks, and preferred Python environment. Ask for an explanation of how results will be validated and how the workflow will be handed over.
Frequently asked questions
Not sure where to start with pandas? These answers cover the essentials.
pandas is used to load, clean, transform, join, and analyze structured data in Python. Companies use it for reporting, exploratory analysis, time-series work, feature preparation, and repeatable data workflows.
pandas handles repeatable transformations, larger datasets, and version-controlled code more effectively than spreadsheets. Spreadsheets remain useful for quick review and manual exploration, while pandas is better suited to automated and auditable workflows.
pandas provides labeled tables and time-series structures, while NumPy focuses on numerical arrays and mathematical operations. They are commonly used together, with pandas providing data handling and NumPy supporting efficient computation underneath.
A strong pandas specialist often brings Python, SQL, NumPy, Jupyter, and data visualization experience. Depending on the project, knowledge of scikit-learn, PyArrow, cloud storage, testing, and workflow orchestration is also valuable.
The right level depends on data complexity, source quality, performance demands, and the importance of the final output. A focused reporting task may need a specialist who can work independently, while production pipelines require deeper skills in testing, optimization, deployment, and data governance.
Yes, pandas work is often well suited to remote collaboration when specialists receive secure access, sample data, documentation, and clear acceptance criteria. On-site collaboration can help when the work depends on undocumented processes or frequent contact with domain teams.
Review whether pandas code is readable, tested, reproducible, and appropriate for the data volume. Ask the specialist to explain validation checks, missing-value decisions, performance trade-offs, and how another team member will maintain the workflow.
pandas may not be the best choice when data is too large for the available memory, transformations must run in a distributed system, or streaming is central to the design. A specialist may recommend SQL, Polars, Dask, Spark, or a database-native approach while keeping pandas for focused analysis.
The average hourly rate of freelancers who have used pandas in their recent projects is 82 €, which corresponds to a daily rate of about 657 € based on an 8-hour working day.
Of the freelancers who have used pandas in their recent projects, 98% hold at least a Bachelor's degree, 80% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers who have used pandas in their recent projects have 12 years of professional experience, with a single engagement typically lasting around 2.7 years.
The most common languages among freelancers who have used pandas in their recent projects are English (99%), German (98%), and French (16%).
The most common industries among freelancers who have used pandas in their recent projects are Information Technology (77%), Education (49%), and Banking and Finance (32%).
The most common business areas among freelancers who have used pandas in their recent projects are Information Technology (88%), Product Development (74%), and Business Intelligence (68%).
Main locations of FRATCH Experts, who have recently used pandas
Our freelancers and interim experts are at home all over Germany — available on-site in Berlin, Hamburg, Munich and every major business hub, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
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Nuremberg 
Vienna Across Switzerland our specialists are active in Zurich, Geneva, Basel and Bern — working on-site or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
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