Exploratory Data Analysis Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Exploratory Data Analysis
Philipp Grunert
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
Anjaneya Marimireddygari
Last position:
Machine Learning Engineer Intern at Slash Mark
- Built and fine-tuned CNN and RNN architectures using transfer learning for real-world classification tasks — core deep learning skills applicable to BMW's multimodal LLM and GenAI vehicle function development.
- Implemented Dropout, Batch Normalisation, and Early Stopping across deep learning experiments; evaluated rigorously using precision, recall, F1-score, and confusion matrices for production-grade reliability.
- Developed an AI-powered attendance management system using LBPH facial recognition, deployed via Flask web interface with real-time SMS notifications — demonstrating end-to-end AI product delivery for real users.
- Collaborated across cross-functional teams to deliver scalable, documented ML pipelines designed for reproducibility — matching BMW's interdisciplinary team and research environment.
- Integrated AI tooling directly into the development workflow from design through to testing, maintaining high velocity without compromising correctness.
Dieter Ratz
Last position:
Driver analyses at Genactis GmbH
- Calculation of attribute importance based on driver analyses
- Interpretation, reporting, and consulting
Michael Serejenkov
Last position:
Data Scientist at CompuGroup Medical Deutschland AG, docmetric GmbH
Development of AI-based and classical models for analyzing medical and patient data, including medication analyses, diagnosis analyses, forecasts, procedure analyses, dosage analyses, comorbidity analyses, prescription analyses, patient potential analyses, and referral profile analyses. Analyses in the area of Real World Evidence.
- Gathering customer requirements
- Planning the subproject
- Designing and defining KPIs
- Designing and developing models and visualizations of the results using customer dashboards
- Developing and implementing DWH adjustments
- Deriving recommendations for action
Methods, technologies: Simulation, Artificial Intelligence, Python, R, SQL, Microsoft Power BI, Amazon Web Services, Elasticsearch, PostgreSQL, Databricks, Multivariate Statistics
Shubham Sahni
Last position:
Commercial Data and Analytics Intern at Bavarian Nordic
- Partner with commercial, sales, and medical affairs teams to translate business questions into structured analyses and interactive Power BI dashboards, enabling data-driven decisions in a regulated pharma environment.
- Design and maintain Power BI dashboards that integrate data from Veeva CRM, SharePoint and Databricks, providing real-time visibility into sales trends, territory performance, and commercial KPIs across multiple markets.
- Query and join multiple tables in Databricks using SQL to build clean, analysis-ready datasets, applying transformations such as filtering, aggregation, and window functions to prepare data for reporting.
- Implement Power Automate flows to automate data refresh processes and trigger alerts for KPI thresholds, improving the timeliness and reliability of commercial analytics reporting.
Minal Borse
Last position:
Business Intelligence Specialist at Coster Special Technologies S.p.A.
- Designed and developed interactive SAP Analytics Cloud (SAC) dashboards and reports for Finance, Supply Chain, Logistics, Procurement, HR, and Manufacturing, covering KPIs such as Profit & Loss, Balance Sheet, Fixed Costs, Headcount, Personnel Expenses, Stock Analysis, OTIF, Production Volume, BOM, Spend, and Compliance to Schedule.
- Built and optimized end-to-end ABAP CDS data models (Basic, Composite, and Consumption Views) using the VDM approach, integrating data from key SAP S/4HANA tables. Strong expertise in ABAP CDS, SQL, SAP data modeling,
- Collaborated with cross-functional teams to define KPI logic, standardized user story templates, resolved BI requests through JIRA, improved reporting performance, and delivered scalable, secure, and business-focused analytics solutions that enhanced decision-making and operational efficiency.
- Trained business stakeholders across various countries on SAP Analytics Cloud (SAC) dashboard usage and developed comprehensive training manuals to promote user adoption and enable self-service analytics.
Polina Schulz
Last position:
Data Migration Lead – Process Automation, Data Engineering & Reporting at Large Public-Sector Bank
Configured and automated data extracts from Oracle databases, achieving 100% data accuracy in a critical migration project, significantly reducing manual errors and accelerating the migration timeline.
Designed and implemented interfaces with Order Management Systems (OMS), enabling seamless and automated data exchange and improving operational efficiency through faster, error-free order processing across business units.
Developed and deployed data extraction workflows to support regulatory compliance and customer reporting, ensuring timely delivery of key reports, reducing manual effort, and increasing customer satisfaction.
Utsav Rabadiya
Last position:
Working Student Junior Data Scientist (Performance Team GT Fleet) at Uniper SE
- Analyzed large-scale power plant data to develop and optimize key performance indicators (KPIs) for fleet-wide performance monitoring.
- Designed and developed interactive Power BI dashboards to provide real-time insights into key business metrics, improving decision-making processes across departments.
- Collaborated with site engineers and asset management to harmonize performance metrics across multiple countries.
- Supported digital transformation initiatives by implementing data-driven use cases using agile project management methods.
- Utilized OSIsoft PI systems for time-series data analysis and visualization to improve operational insights.
Albert Frischmann
Last position:
Lead Product Owner at CMBlu Energy AG
- Lead Product Owner for 4 development teams
- Leading and coordinating a greenfield project with parallel implementation of core components by independent teams; managing dependencies and resources
- Establishing a data lakehouse approach, including analysis of data volumes and future requirements as part of a cloud migration (best-of-breed approach)
- Responsible for requirements analysis, selection, and piloting of a LIMS/ELN system, supported by advising decision-makers and managing external vendors
- Introducing and managing an OpenWeb UI and Azure OpenAI-based RAG system to support knowledge extraction and data-driven analyses
- Setting up, configuring, and managing Jira projects, as well as developing project-specific workflows and automations
- Implementing classic Scrum processes with all ceremonies and taking on the Scrum Master role for all involved teams
- Assisting in hiring through interviews and assessments from a product owner's perspective
- Making key architectural decisions, including selecting the platform for the data lakehouse (Databricks) and the strategic integration of LIMS and analytics platforms
Jovan Jelic
Last position:
CSV Manager, Technical Engineering at CureVac Printer GmbH
- Assist with the development of system requirements and specifications to ensure requirements are testable and 21 CFR Part 11 requirements are met
- Coach implementation teams in the proper execution of validation documents
- Evaluate proposed changes to validated computer systems and recommend level of validation activities required
- Coordinate audits of internal computer systems validation activities, protocols and procedures, and prepare responses
- Identify and qualify all computer systems impacting cGMP operations using a risk-based methodology
- Develop CFR Part 11 computer systems validation plans, qualification test protocols, traceability matrices, reports, IQ/OQ protocols and all deliverables within the scope of the validation plan
- Develop and maintain test plans, test scripts and user acceptance tests and manage their execution
- Act as CSV lead for all validation projects and execute or oversee validation plans and documents
- Perform project management activities for the CSV process within the scope of system projects
- Work with project manager to include validation activities in implementation timelines
- Manage internal CSV resources to facilitate completion of qualification activities
- Ensure initiation, preparation and closeout of all CSV-related deviations, discrepancies and change control documents
- Work closely with Validation Manager and QA Compliance to ensure appropriate validation of cGMP computer systems
- Conduct or facilitate validation and 21 CFR Part 11 training
Uzair Arshed
Last position:
Data Scientist at Taurva Solutions
- Collect, clean, and preprocess data.
- Perform exploratory data analysis to find patterns and insights.
- Build and evaluate statistical models and machine learning algorithms.
- Visualize data and results using tools like Matplotlib, Seaborn, Power BI, or Tableau.
- Work with cross-functional teams to define data needs and KPIs.
- Develop models using frameworks such as TensorFlow, PyTorch, and Scikit-learn.
- Follow data privacy and security regulations.
Athul Sivan
Last position:
Data Scientist at Science to Data Science – Deutsche Welle
- Built a GPT-based synthetic data pipeline that reduced acquisition cost and turnaround time by more than half.
- Modeled audience behavior across underrepresented groups using prompt workflows and statistical validation.
- Evaluated data realism with clustering, regression, and divergence analysis.
- Delivered reproducible Python workflows to automate experimentation in an Agile environment.
- Translated analytical results into clear insights for content and strategy teams.
- Technologies and skills: Python, Generative AI, GPT, Machine Learning, exploratory data analysis, Agile, GitHub, cloud computing, hallucination analysis.
Javid Hasanov
Last position:
Research Assistant (Application Project - CHAI) at FH Kiel & Christian-Albrechts-Universität zu Kiel (CAU)
- Developing an AI-based corrosion detection system for maritime infrastructure as part of the CHAI Research Project.
- Built a binary image classification model to detect corrosion using a dataset of 5,000+ metal surface images.
- Automated data labeling from segmentation masks and designed bounding box generation workflows for individual corrosion areas.
- Conducted data preprocessing, data augmentation, and model evaluation (accuracy, precision, recall, F1-score).
- Collaborated with the research team to integrate computer-vision workflows for corrosion monitoring and dataset enhancement.
Martin Svítek
Last position:
Business Intelligence Data Analyst at webeet
- Optimized SQL data pipelines for clean insights.
- Analyzed and visualized trends with Python.
- Improved dashboards and automations.
- Worked with Google Sheets, GCP, Snowflake, dbt, Spreadsheets/Excel, Databricks, PySpark, and Fivetran.
Nina Nowak
Last position:
ESG Data Analyst (Volunteer, part-time) at Climate Accountability API
- Development and validation of a data model and ESG rating pipeline
- GenAI governance
Discover over 15,000 top freelancers
Statistics of experts using Exploratory Data Analysis
Aggregated from the professional profiles of matched freelancers.
Experience
12 years
Position duration
2.2 years
Positions per freelancer
7
Top business areas
Business Intelligence, Information Technology, Research and Development
Top industries
Information Technology, Education, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
100%
Master's degree or higher
77%
Doctorate
19%
Certifications per freelancer
2
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Germany 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.
Average rates of experts in Germany using Exploratory Data Analysis
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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What EDA means
Exploratory Data Analysis, often called EDA, is the work of inspecting data before a firm conclusion or model goes live. It helps teams understand distributions, missing values, anomalies, and relationships across fields. Strong exploratory data analysis turns a raw table into a clear set of questions and next actions.
Typical work
- Profile datasets and check structure, types, and completeness
- Find outliers, skew, drift, and inconsistent labels
- Compare segments, cohorts, and time-based patterns
- Prepare charts and concise notes for product, analytics, or science teams
- Define follow-up checks before dashboards or models are built
Tools and habits
EDA usually lives in Python, R, SQL, and notebooks such as Jupyter or RStudio. Specialists often use pandas, NumPy, ggplot2, seaborn, and BI tools for quick visual checks. Good work is reproducible, well commented, and tied to the business question rather than to one pretty chart.
When to bring in help
Companies look for freelance EDA specialists when data is messy, a project starts without clear assumptions, or internal teams need a fast second opinion. This is common before forecasting, segmentation, churn work, experimentation, or feature design. In Germany, it often helps when teams need clear English documentation for mixed local and international groups.
What strong specialists do
A strong expert asks the right questions before touching the data. They know how to read patterns without overclaiming, separate signal from noise, and explain limits clearly.
- Spot data issues that would distort later analysis
- Choose charts and summaries that fit the data shape
- Connect findings to decisions, not just observations
- Leave behind clean notebooks, notes, and reusable checks
Adjacent skills
EDA often sits next to data cleaning, feature engineering, statistics, and model validation. Many projects also need SQL, version control, and a solid grasp of the source system, whether that is a warehouse, an API feed, or event data. The best specialists move comfortably between exploration and the next technical step.
Frequently asked questions
Quick answers to the questions that come up most around Exploratory Data Analysis.
Exploratory Data Analysis is used to understand what a dataset really contains before anyone makes decisions or builds models. It reveals missing values, unusual records, shifts in distribution, and relationships that deserve more attention. That makes later work in reporting, forecasting, and machine learning much more reliable.
EDA overlaps with both, but it is broader than either one. Data cleaning fixes known issues, while visualization shows selected views; EDA uses both to investigate the data and form hypotheses. A good specialist does not stop at charts and does not treat cleanup as the whole job.
A company should bring in exploratory data analysis support when the data is new, messy, or not yet trusted. It is also useful when a team needs a fast review before a launch, a model, or a board discussion. Freelance help works well when the internal team has the data but lacks time for deep investigation.
A Exploratory Data Analysis specialist commonly works with Python, R, SQL, and notebooks such as Jupyter or RStudio. The exact toolset depends on the data source and the audience, but pandas, seaborn, ggplot2, and SQL are common. Strong specialists can also use BI tools when a quick business-facing view is needed.
The best EDA work depends on solid data wrangling, statistics, and clear communication. Many projects also benefit from feature engineering, experiment design, and familiarity with the warehouse or source system. Without those skills, exploration can become a pile of charts with little business value.
A small exploratory data analysis task can be handled by a specialist who knows the data domain and the tools well. More complex work needs someone who can deal with messy pipelines, mixed data types, and ambiguous questions. The key is not a title level, but the ability to turn raw inputs into useful insight.
Yes, Exploratory Data Analysis is often done remotely because the work centers on data access, notebooks, and review sessions. For teams in Germany, remote work is common when the data is already documented and the communication is clear. On-site time can help if the source systems are complex or stakeholders need close workshop-style collaboration.
A strong EDA specialist shows how they think, not just the final chart. Look for clean notebooks, clear comments, careful handling of missing or odd data, and answers that lead to action. The best work explains trade-offs, flags uncertainty, and stays close to the real business question.
The average hourly rate of freelancers in Germany who have used Exploratory Data Analysis in their recent projects is 81 €, which corresponds to a daily rate of about 646 € based on an 8-hour working day.
Of the freelancers in Germany who have used Exploratory Data Analysis in their recent projects, 100% hold at least a Bachelor's degree, 77% hold at least a Master's degree, and 19% hold a doctorate.
On average, freelancers in Germany who have used Exploratory Data Analysis in their recent projects have 12 years of professional experience, with a single engagement typically lasting around 2.2 years.
The most common languages among freelancers in Germany who have used Exploratory Data Analysis in their recent projects are German (100%), English (100%), and French (14%).
The most common industries among freelancers in Germany who have used Exploratory Data Analysis in their recent projects are Information Technology (71%), Education (46%), and Professional Services (39%).
The most common business areas among freelancers in Germany who have used Exploratory Data Analysis in their recent projects are Business Intelligence (93%), Information Technology (75%), and Research and Development (68%).
Main locations of FRATCH Experts, who have recently used Exploratory Data Analysis
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.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
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Philipp Thomaschewski
FRATCH CEO
