pandas Experts in Zurich
matched in minutes with vetted, available freelance specialistsHire experts who clean, transform and analyze structured data with pandas, connect workflows to NumPy and SQL, and deliver reliable reporting datasets and machine learning inputs. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Zurich, who have recently used pandas
Paulo M.
Last position:
Power BI & Microsoft Fabric BI Specialist at Financial Bank
- Developed Power BI semantic models and DAX measures, reducing financial reporting time.
- Implemented RLS, strengthening role-based data access and governance controls.
- Delivered automated KPI dashboards, accelerating reporting and decision-making.
- Designed a Fabric Lakehouse with Bronze, Silver and Gold layers, improving data processing.
- Built Fabric pipelines and PySpark transformations, reducing processing time by 40%.
- Built and optimized Power BI semantic models, improving report performance, scalability and usability.
- Optimized UNIX universes, reducing query execution time by 40%.
Environment: Power BI (Semantic Models, DAX, RLS), Microsoft Fabric (Lakehouse, Pipelines, PySpark), SAP BO 4.3/2025, Web Intelligence, IDT/UNX, InfoBurst, 360Suite, GitHub, JIRA.
Mohamad K.
Last position:
Senior Backend Developer at Standing on Giants
- Led architecture and end-to-end engineering delivery for community-driven SaaS platforms serving 2M+ monthly active users.
- Architected and led the migration of a monolithic Python/FastAPI and PostgreSQL database and LangChain with codebase to an event-driven microservices architecture on AWS EKS, sustaining 10x traffic growth from ~150 RPS to 1,500+ RPS with zero re-architecture cycles.
- Defined and enforced engineering standards across services including API contracts, observability baselines, and deployment topology, reducing production incidents by 55% and MTTR from 2 hours to under 25 minutes within 9 months.
- Redesigned the caching and query layer using multi-tier Redis caching and database indexing/partitioning, cutting p95 API latency from 850ms to 180ms (78% reduction) and database CPU load by 45%.
- Built CI/CD platform on GitHub Actions, Terraform, and Kubernetes (EKS) with blue-green and canary rollouts, increasing deployment frequency from ~2/month to 8-12/day and reducing lead time from 10 days to under 6 hours.
- Implemented contract testing, automated load testing, and observability SLOs using Prometheus, Grafana, and OpenTelemetry, raising platform availability from 99.5% to 99.95% (10x reduction in error budget burn).
- Led and grew a cross-functional team of 8 engineers across backend, frontend, and DevOps, scaling headcount from 4 to 8 with 85% retention; owned hiring, onboarding, performance reviews, and career development.
- Partnered with Product, Design, and Client Success leadership as primary technical decision-maker; translated business goals into technical roadmaps and drove build-vs-buy decisions on authentication, search, and AI tooling.
- Introduced AI-assisted development workflows including automated code review and a RAG-based internal knowledge assistant using Graph (GraphRAG, Neo4J), increasing sprint throughput by 30% across two quarters.
- Owned incident command and production support rotation; established runbooks, postmortem culture, and on-call SLOs, reducing weekend paging incidents by 70%.
- Developed and optimized Algorithms using python libraries like Numpy and Pandas.
Gwang Jin K.
Last position:
Data Scientist / Applied AI, Automation & Data Systems Researcher at Independent
- Built and explored applied GenAI, RAG, GraphRAG, local LLM, agentic AI and document-intelligence prototypes for structured analysis, evidence extraction, semantic search, technical reasoning and decision-useful reporting
- Developed private local-LLM workflows and AI system patterns focused on privacy, reproducibility, reviewability, low-cost inference and practical user control
- Built reproducible Python/R workflows for data analysis, automation, API-driven tooling, validation logic, technical documentation and AI-assisted software development
- Designed workflows around explicit assumptions, traceable inputs, reviewable outputs and failure-mode awareness rather than black-box “looks good” demonstrations
- Supported RAHN AG in a chemical/regulatory environment with data extraction and processing around WERCS, a regulatory application for chemical product and compliance data
- Explored complex application/database schemas and wrote nested SQL queries to extract information for mixture calculations, component relationships, regulatory rules and reporting logic
- Continued hands-on development in Git/GitHub/GitLab/Bitbucket, Docker/Linux deployment patterns, REST/API workflows, error handling, technical writing and fast AI-assisted prototyping
- Built technical writing and documentation workflows that turn complex systems into clear runbooks, checklists, decision notes and user-facing explanations
Georgios S.
Last position:
Senior Software Engineer at UBS Bank
- Implementations of a code refactoring framework able to refactor thousands of repositories leveraging Generative AI
- Use Python (Django, Flask, FastAPI), Java and Typescript in Azure Cloud (Data Lake, VMs, AI) and GitLab infrastructure
- Mentoring and pair programming
- Obtained Azure AI-900 and AI-102 certifications
Ursula M.
Last position:
Business Mentor at RoleModel Rebels
- Mentor female students and professionals in advancing their careers, particularly as aspiring tech entrepreneurs.
Alejandro A.
Last position:
AI Researcher & Engineer at Tufa Labs
- Deployed and optimized the inference stack on a multi-node DGX B200 cluster across vLLM and SGLang (serving, throughput and latency tuning).
- Built, with a small team, an internal Python library for LM pretraining covering the full training loop: distributed training with PyTorch FSDP, data pipelines, checkpointing, config and hyperparameter management, and experiment tracking.
- Built and evaluated agent scaffolds on interactive game benchmarks similar to ARC-AGI-3, with metrics for how models plan, explore and adapt across multi-step episodes; classified model errors and fed the findings back into scaffold and evaluation design.
- Researched looped transformer architectures.
Karl E.
Last position:
incl. CI/CD, automation at AALS Software AG
- Designed and delivered a practical real-time course on Flink and Hadoop with MapReduce, HDFS, Spark, Flink, Hive, HBase, MongoDB, Cassandra, and Kafka
- Gained extensive DevOps and CI/CD experience
- Created ETL/ELT pipelines with Apache tools and Pentaho
- Led projects in municipal software, financial services, and big data with Kafka
- Developed AI/NLP models and chatbots with RASA, Chatter, and Dialogflow
- Built and managed a TypeDB knowledge database
- Worked with OpenStack, Kubernetes, and Podman
Discover over 15,000 top freelancers
Statistics of experts using pandas
Aggregated from the professional profiles of matched freelancers.
Experience
23 years
Position duration
2.6 years
Positions per freelancer
9
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Banking and Finance, Education
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
100%
Master's degree or higher
71%
Doctorate
29%
Certifications per freelancer
2
Most common languages
English, German, French
Speak two or more languages
100%
Based on our profile pool as of 16 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Zurich 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 Zurich 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 16 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 (100%)
- Banking and Finance (86%)
- Education (71%)
- Healthcare (43%)
- Retail (43%)
- Telecommunication (43%)
- Energy (29%)
- Manufacturing (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What pandas does
pandas is an open-source Python library for working with tabular and time-series data. Its DataFrame and Series structures make it practical to load, inspect, filter, join, reshape and summarize information from many sources. Companies use it to turn raw data into analysis-ready datasets, operational reports and inputs for statistical or machine learning work.
Core capabilities
pandas specialists handle the full path from source files to trusted outputs. They work with CSV, Excel, JSON, Parquet, SQL queries and APIs, while accounting for missing values, inconsistent types, duplicate records and changing schemas. Typical deliverables include reusable notebooks, transformation scripts, validation checks and documented data preparation pipelines.
Ecosystem and tooling
pandas sits within the Python data ecosystem. Strong professionals commonly combine it with NumPy for numerical operations, Matplotlib or Seaborn for visual exploration, and Jupyter for interactive analysis. They may also connect it to SQLAlchemy, PyArrow, scikit-learn, Apache Spark or cloud storage when datasets, integrations or production workloads require more than a local DataFrame.
When to bring in specialists
Freelance expertise is useful when internal teams need dependable data work without slowing a product or reporting project. Common signals include:
- Replacing fragile spreadsheet-based reporting with repeatable transformations
- Reconciling data from finance, sales, research or operational systems
- Preparing clean features for forecasting or machine learning
- Investigating slow notebooks, memory pressure or inconsistent results
pandas in Zurich
Zurich companies may use pandas in finance, insurance, life sciences, retail and industrial analytics. A freelance specialist can work remotely through clear notebooks, tests and documentation, or collaborate on site when data access, stakeholder workshops or regulated workflows require close coordination. German and English communication may both matter, depending on the team and documentation standards.
What strong professionals deliver
Quality goes beyond knowing DataFrame syntax. Experienced pandas professionals define data contracts, preserve types and time zones, make joins explicit, and test edge cases such as empty inputs or duplicated keys. They profile memory and runtime, avoid opaque notebook state, separate exploration from production code, and explain assumptions so another team can maintain the result.
Frequently asked questions
Quick answers to the questions that come up most around pandas.
pandas is used to load, clean, transform and analyze structured data in Python. Companies rely on it for reporting datasets, exploratory analysis, time-series work, reconciliation and preparation of inputs for statistical models or machine learning.
pandas provides labeled tables and convenient operations for missing values, joins, grouping and reshaping. NumPy is better suited to low-level numerical arrays, while SQL is usually preferable for filtering and aggregating data close to a database; a strong specialist knows when to combine all three.
A capable pandas specialist should understand Python, SQL, data modeling and source-system behavior. Useful adjacent skills include NumPy, Jupyter, testing, Git, visualization libraries, PyArrow and orchestration or cloud tools when the work must run repeatedly outside a notebook.
A small analysis may need focused pandas experience, while a production pipeline calls for broader knowledge of testing, packaging, deployment and data quality. Assess the complexity of the sources, transformations, performance needs and handover requirements rather than relying on a generic experience label.
pandas work is often well suited to remote collaboration when access, data samples and acceptance criteria are defined clearly. Shared repositories, reproducible notebooks, documented assumptions and automated checks help teams work effectively across Zurich and other locations.
pandas can become unsuitable when data volume, concurrency or operational reliability exceeds what a local or single-process workflow can handle. A specialist may recommend SQL pushdown, Dask, Polars or Apache Spark, depending on the workload and the existing environment.
Review whether pandas transformations are reproducible, tested and clear about types, keys, missing values and time zones. Ask for evidence of validation, performance profiling, readable structure and documentation that explains both the source data and the intended output.
Before using pandas, clarify the data sources, expected output, refresh process, access constraints and definition of correctness. It is also important to agree on whether the deliverable is an exploratory notebook, a reusable Python module, a scheduled pipeline or a documented dataset for another team.
The average hourly rate of freelancers in Zurich, Switzerland who have used pandas in their recent projects is 97 €, which corresponds to a daily rate of about 773 € based on an 8-hour working day.
Of the freelancers in Zurich, Switzerland who have used pandas in their recent projects, 100% hold at least a Bachelor's degree, 71% hold at least a Master's degree, and 29% hold a doctorate.
On average, freelancers in Zurich, Switzerland who have used pandas in their recent projects have 23 years of professional experience, with a single engagement typically lasting around 2.6 years.
The most common languages among freelancers in Zurich, Switzerland who have used pandas in their recent projects are English (100%), German (86%), and French (43%).
The most common industries among freelancers in Zurich, Switzerland who have used pandas in their recent projects are Information Technology (100%), Banking and Finance (86%), and Education (71%).
The most common business areas among freelancers in Zurich, Switzerland who have used pandas in their recent projects are Information Technology (100%), Product Development (71%), and Research and Development (71%).
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.
Countries:
Zurich 
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