pandas Expert in Switzerland
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Meet FRATCH Experts in Switzerland, 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
Hrvoje K.
Last position:
Researcher, Investment Research Institute at Pictet Group
- Designed and implemented AI-enhanced macro-factor models improving asset allocation decisions across multi-asset portfolios
- Developed machine learning-based FX trading strategies, integrating macroeconomic and sentiment data to identify profit opportunities
- Models adopted by the investment committee to guide equity and currency exposure
- Collaborated with portfolio managers to translate analytical output into actionable investment insights
Discover over 15,000 top freelancers
Statistics of experts using pandas
Aggregated from the professional profiles of matched freelancers.
Experience
21 years
Position duration
2.6 years
Positions per freelancer
9
Top business areas
Information Technology, Research and Development, Product Development
Top industries
Banking and Finance, Information Technology, Education
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
100%
Master's degree or higher
75%
Doctorate
38%
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 Switzerland 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 Switzerland 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.
- Banking and Finance (88%)
- Information Technology (88%)
- Education (75%)
- Healthcare (50%)
- Manufacturing (38%)
- Retail (38%)
- Telecommunication (38%)
- Automotive (25%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
DataFrame foundation
pandas is an open-source Python library for working with structured data. Its core objects, DataFrame and Series, support filtering, joining, reshaping, grouping and time-based analysis. Companies use Python pandas to turn files, database extracts and API responses into reliable datasets for reporting, forecasting and machine learning.
Data preparation
The library handles practical data work that often determines whether an analysis can be trusted. Specialists use it to standardize columns, manage missing values, remove duplicates, convert types and validate business rules before data reaches a model, dashboard or decision process.
- Combine CSV, Excel, JSON and database extracts
- Reshape wide and long datasets
- Clean dates, categories and inconsistent values
- Create repeatable validation steps
Python ecosystem
pandas works closely with NumPy for numerical operations, Jupyter for exploration and Matplotlib or Plotly for visualization. Strong specialists also connect it with SQL, cloud storage, scikit-learn, Apache Parquet and orchestration tools. They know when vectorized operations are preferable and when another tool is better for large-scale processing.
Business applications
Teams use pandas in finance, life sciences, manufacturing, retail and public services for recurring analysis and operational reporting. In Switzerland, projects may combine local business data with remote collaboration across German-, French- or Italian-speaking teams, so clear documentation and agreed data definitions matter.
- Prepare data for forecasting and machine learning
- Reconcile financial or operational records
- Explore customer, product and transaction data
- Produce analysis-ready exports and reports
When to hire expertise
Freelance expertise helps when a one-off analysis must become a dependable workflow, when spreadsheets no longer scale, or when several source systems need to be reconciled. It is also useful during migrations from manual reporting to Python-based pipelines, or when an internal team needs support with performance, testing and handover.
Quality signals
A capable pandas professional explains assumptions, preserves source data and makes transformations easy to audit. Look for tested functions, meaningful column names, explicit handling of missing and duplicate records, and sensible use of indexes and data types. For larger datasets, ask how memory use, chunking, file formats and query pushdown will be handled.
Frequently asked questions
The facts hiring teams ask for most often when it comes to pandas.
pandas is used to load, clean, transform and analyze tabular data in Python. Companies use it for reporting, reconciliation, exploratory analysis, feature preparation and repeatable data workflows.
pandas is more reproducible and flexible than spreadsheet-based analysis, especially when transformations must be tested or repeated. SQL is usually stronger for filtering and aggregating data inside a database, while pandas is useful after extraction and for combining sources, statistical preparation and Python-based modeling.
A strong pandas specialist commonly works with Python, NumPy, SQL, Jupyter and Git. Depending on the project, experience with scikit-learn, Parquet, cloud storage, workflow orchestration and visualization libraries is also valuable.
The right level depends on data complexity rather than the library alone. A focused cleanup may need solid Python and pandas fundamentals, while a production workflow calls for experience with testing, performance, data validation, source systems and maintainable handover.
pandas can handle substantial datasets when professionals use suitable dtypes, selective columns, efficient formats and chunked processing. For data that exceeds available memory or requires distributed execution, they may combine it with SQL engines, Dask, Spark or another scalable processing system.
pandas projects are often well suited to remote collaboration because code, notebooks, tests and sample data can be reviewed asynchronously. Swiss teams should still define access controls, data residency requirements, working hours and language expectations, especially when several locations or languages are involved.
Ask for a clear explanation of data assumptions, edge cases and validation checks in a pandas workflow. Review whether transformations are tested, source data remains traceable, missing values are handled deliberately and the result can be rerun without manual notebook steps.
pandas can be part of a production pipeline when its scope and resource needs are understood. A good specialist adds versioned code, tests, logging, schema checks and reliable inputs, then chooses SQL, Polars or distributed tools when the workload makes pandas a poor fit.
The average hourly rate of freelancers in Switzerland who have used pandas in their recent projects is 101 €, which corresponds to a daily rate of about 810 € based on an 8-hour working day.
Of the freelancers in Switzerland who have used pandas in their recent projects, 100% hold at least a Bachelor's degree, 75% hold at least a Master's degree, and 38% hold a doctorate.
On average, freelancers in Switzerland who have used pandas in their recent projects have 21 years of professional experience, with a single engagement typically lasting around 2.6 years.
The most common languages among freelancers in Switzerland who have used pandas in their recent projects are English (100%), German (88%), and French (50%).
The most common industries among freelancers in Switzerland who have used pandas in their recent projects are Banking and Finance (88%), Information Technology (88%), and Education (75%).
The most common business areas among freelancers in Switzerland who have used pandas in their recent projects are Information Technology (88%), Research and Development (75%), and Product Development (63%).
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.
Zurich Request a free demo
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