Data Mining Experts in Frankfurt
matched in minutes from over 15,000 CVs with the power of AIHire experts who uncover patterns in large datasets, design predictive models and prepare reliable data pipelines with tools such as Python, SQL, Spark and scikit-learn. FRATCH matches you quickly and precisely with vetted, available freelancers.
Meet FRATCH Experts in Frankfurt, who have recently used Data Mining
Eric B.
Last position:
Quality Assurance Lead (QSV) at Federal Employment Agency
Supported the International Web Presence project of the Federal Employment Agency (IntWeb) in quality management, taking on responsibility for the quality of processes and project deliverables while adhering to BA standards. The project's main goals are to give professionals abroad a quick overview of their chances to move to Germany and to enable them to take the necessary steps in a consistently digital way.
Set the fundamental guidelines using the QA handbook
Summarized test results in QA reports for PLA
Analyzed project outcomes for improvement opportunities
Quality management of requirements analysis (especially processes, methods and tools)
Ensured compliance with SERA guidelines
Created a cross-project test concept
Agreed on sprint completion reports
Conducted formal reviews of deliverables according to guidelines and/or project plan
Acted as contact person for internal audit and external audits by auditors or the Federal Audit Office (BRH)
Technologies: JIRA, Confluence, MS Office, GitLab, Kubernetes
Jörn B.
Last position:
Project Management and PMO at TANKKARTEN | PAYMENT
- Close collaboration with development teams to ensure alignment with project goals
- Assessment of in-scope assets, requirements definition and support of the public tender process up to vendor selection
- Creation and maintenance of UML sequence diagrams to document system interactions
- Intensive use of Agile/Scrum methodology and the Spotify model
- Development and implementation of cutover and rehearsal plans together with the application managers
Olusina F.
Last position:
Cyber Job Simulation at Deloitte Australia
- Completed a job simulation involving reading web activity logs.
- Supported a client in a cybersecurity breach.
- Answered questions to identify suspicious user activity.
Ralitsa P.
Last position:
Process, Project and Requirements Manager, Client Project @ RUBINLAKE at 1&1 Versatel GmbH
- Introduction of a new workflow tool for fibre connectivity & broadband delivery
- Requirements design, estimation and prioritization
- Creation of rollout and project plans
- Managing the development team using Scrum
- Design and project management of the API interface to existing tools
- Process modeling for the new order dispatching teams (FCD & BD)
- Designing processes inside and outside the team
- Requirements design and implementation of necessary tools
- Creation and calculation of production KPIs
Michael W.
Last position:
Business Analyst, Product Owner, Deputy Chairman of the Advisory Board at Federal Ministry, large German city
- We designed a networking platform to improve cooperation and information in the district (Project 71).
- We carried out a tender, defined necessary documents and processes.
- We aimed to ensure that the commissioned service provider carries out quality assurance of the project team's results before implementation.
- We selected a provider.
Ritika S.
Last position:
AWmOpsRtKekEX(CPEliRenIEtN: CInEfoSrs.yDs,aHtaitAarcchhiiEtencetr(gAyW) S)
Global marketing analytics for Hitachi Energy as part of a global data modernization initiative aiming to enhance data retention, historical data availability and provide Eloqua's 2-year retention for remote interaction reporting and analytics.
Analyzed Eloqua's default retention policy and identified risk of data loss for records older than two years.
Designed and implemented historical data preservation strategy by creating transformed tables in the target data platform to archive older data while ensuring data quality dashboards.
Collaborated with the Power BI team to re-point dashboards from raw Eloqua imports to the newly created archival layer.
Leveraged Jira to track and manage data engineering tasks, bugs, and feature requests across Agile sprints; coordinated backlog prioritization and task assignment to align data pipeline development with business needs.
Power BI dashboard optimization:
Worked closely with business stakeholders to assess and understand reporting needs for reverse customer data.
Designed and implemented incremental refresh in Power BI to ensure daily updates without full data reloads.
Collaborated with Azure data engineers to optimize data processing and publication pipelines.
Stakeholder communication & data modeling:
Acted as liaison between Group Data Office and Technology Office to align data modelling standards.
Gathered requirements from data engineering team and participated in weekly status meetings to provide implementation updates and resolve blockers across teams in Germany, Poland, and India.
Documentation & quality assurance:
Prepared end-to-end technical design documentation, data flow diagrams, and Power BI audit guides for future reference.
Participated in UAT sessions with business users to validate data outputs and report accuracy.
Daniel S.
Last position:
Business and IT consulting at Business and IT Consulting (freelance)
- Process consulting
- Project management
- Creating and aligning functional and technical specifications
- Portfolio management
- Stakeholder management
- Data cleansing
- Test, quality, and requirements management
- Rollout management
- Financial planning
- Marketing and sales consulting
- IT architecture and strategy consulting
- Trainer for IT, project management, and eBusiness
Discover over 15,000 top freelancers
Statistics of experts using Data Mining
Aggregated from the professional profiles of matched freelancers.
Experience
20 years (Germany: 22 years)
Position duration
2.7 years (Germany: 2.6 years)
Positions per freelancer
17 (Germany: 14)
Top business areas
Information Technology, Business Intelligence, Marketing
Top industries
Information Technology, Banking and Finance, Professional Services
Certification focus areas
Project Management, Business Intelligence, Information Technology
Bachelor's degree or higher
100% (Germany: 95%)
Master's degree or higher
57% (Germany: 77%)
Certifications per freelancer
3
Most common languages
German, English, Bulgarian
Speak two or more languages
100%
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Frankfurt 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 Frankfurt using Data Mining
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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Data Mining 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 (57%)
- Professional Services (57%)
- Automotive (43%)
- Education (43%)
- Energy (43%)
- Healthcare (43%)
- Insurance (43%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Data Mining does
Data Mining extracts useful patterns, relationships and signals from large or complex datasets. It combines statistics, machine learning, database methods and domain knowledge to support decisions, forecasts and automation. Typical results include customer segments, anomaly alerts, recommendation logic and risk indicators.
Typical project work
Companies use Data Mining to turn raw records into repeatable business processes. Experts may deliver:
- Customer segmentation and churn analysis
- Fraud, compliance and anomaly detection
- Demand forecasting and recommendation models
- Feature extraction from text, logs or transactions
- Dashboards and decision-ready data products
Ecosystem and methods
A strong specialist works across data preparation, exploration, modelling and validation. Common tools include Python, pandas, NumPy, SQL, scikit-learn, Jupyter and Apache Spark. Depending on the workload, projects may also involve cloud data warehouses, distributed processing, APIs, orchestration and model monitoring.
When freelance expertise helps
Companies often bring in an expert when internal teams have valuable data but no clear analysis path, or when a proof of concept must become a dependable service. Frankfurt businesses in finance, logistics, manufacturing, retail and healthcare may need support connecting domain data with scalable analytical workflows. Freelancers can also add focused capacity during migrations, audits or product launches.
What strong experts deliver
Quality starts with a precise business question and a realistic view of the available data. Strong professionals check data quality, prevent leakage, choose suitable evaluation methods and explain limitations in clear language. They document assumptions, make pipelines reproducible and consider privacy, access control and ongoing maintenance rather than delivering an isolated model.
Working with a specialist
A project brief should describe the decision to improve, the data sources, expected output and operational constraints. Remote collaboration works well when repositories, sample data, documentation and review routines are organised; on-site work in Frankfurt can help with workshops and access-sensitive systems. Look for evidence of comparable datasets, transparent reasoning and deliverables that another team can operate.
Frequently asked questions
Key details about Data Mining, drawn from the questions we get asked most.
Data Mining is used to discover patterns in structured and unstructured data. Companies apply it to segmentation, forecasting, fraud detection, recommendations, quality analysis and operational planning.
Data Mining is a broader process for discovering useful patterns, from data preparation and exploration through modelling and interpretation. Data analysis may focus on explaining existing results, while machine learning supplies methods for prediction and automated decision-making within a mining workflow.
A capable Data Mining expert often combines Python, SQL, statistics and data visualisation with knowledge of machine learning. Experience with pandas, scikit-learn, Apache Spark, cloud warehouses, APIs and data pipeline design is useful when findings must reach production systems.
The right level depends on the data quality, business risk and delivery scope, not on a fixed number of years. For a focused analysis, a specialist with relevant dataset experience may be enough; production use usually calls for proven work in validation, deployment, monitoring and documentation.
Yes, Data Mining projects can be handled remotely when secure data access, documentation and review processes are in place. On-site sessions in Frankfurt can still help with stakeholder workshops, sensitive environments and alignment on business definitions.
Ask a Data Mining specialist to explain how they would frame the business question, inspect the data and validate findings. Look for clear assumptions, leakage controls, reproducible pipelines, meaningful evaluation criteria and an explanation that non-specialists can use.
A Data Mining engagement may include a cleaned dataset, exploratory analysis, feature definitions, notebooks, a validated model, pipeline code and documentation. The deliverables should also state limitations, handover steps and how results will be monitored or refreshed.
Data Mining is commonly treated as a core stage of Knowledge Discovery in Databases, often abbreviated KDD. KDD covers the wider process of selecting, cleaning, transforming and interpreting data, while mining focuses on finding patterns or models within it.
The average hourly rate of freelancers in Frankfurt, Germany who have used Data Mining in their recent projects is 99 €, which corresponds to a daily rate of about 790 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used Data Mining in their recent projects, 100% hold at least a Bachelor's degree and 57% hold at least a Master's degree.
On average, freelancers in Frankfurt, Germany who have used Data Mining in their recent projects have 20 years of professional experience, with a single engagement typically lasting around 2.7 years.
The most common languages among freelancers in Frankfurt, Germany who have used Data Mining in their recent projects are German (100%), English (100%), and Bulgarian (14%).
The most common industries among freelancers in Frankfurt, Germany who have used Data Mining in their recent projects are Information Technology (100%), Banking and Finance (57%), and Professional Services (57%).
The most common business areas among freelancers in Frankfurt, Germany who have used Data Mining in their recent projects are Information Technology (100%), Business Intelligence (86%), and Marketing (71%).
Main locations of FRATCH Experts, who have recently used Data Mining
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 Request a free demo
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