Data Mining Experts in Frankfurt
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Meet FRATCH Experts in Frankfurt, who have recently used Data Mining
Eric B.
Last position:
Quality Assurance Manager (QSV) at Federal Employment Agency
Supporting the International Web Presence of the BA (IntWeb) project in the area of quality management, with the aim of taking responsibility for the quality of processes and project results while considering BA standards. The main objectives of the project are to give professionals abroad a quick overview of their opportunities for moving to Germany and to enable them to take the necessary steps consistently and with digital support by providing access to services.
Defining the general guidelines in the QA handbook
Summarising test results in QA reports for PLA
Analysing project results for improvement opportunities
Quality management of requirements analysis (particularly processes, methods, and tools)
Ensuring compliance with SERA requirements
Creating a cross-project test concept
Agreeing on sprint completion reports
Formal review of work results in accordance with requirements and/or the project plan
Contact person for internal audit, external audits by auditors, or BRH (Federal Court of Audit)
Technologies: JIRA, Confluence, MS Office, GitLab, Kubernetes
Bardiya B.
Last position:
Data Scientist at Rewe Digital GmbH
Statistical Forecasting Algorithm
- Improvement of an statistical probabilistic forecasting algorithm for sales + evaluation
- Migration from R/On-premise to Python/Snowflake
- Productionalization on Snowflake in cooperation with data engineers & DevOps
Monitoring Dashboard
- Data engineering for preparation & provisioning of necessary data/resources on Snowflake
- Development & deployment of a Streamlit dashboard in Snowflake
ML-based Probabilistic Forecasting on Vertex AI
- Development of a ML-based probabilistic forecasting algorithm from scratch
- Implementation of MLOps pipeline in Kubeflow on Google Cloud Vertex AI
Tech Stack: Python, Snowflake/Snowpark, R, Streamlit, Gitlab/Gitlab CICD, Terraform, Google Cloud, Vertex AI (aiplatform SDK, gcloud CLI, feature store, model registry, etc), kubeflow
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.
Jörn B.
Last position:
Project Management and PMO at TANKKARTEN | PAYMENT
- Close collaboration with development teams to ensure alignment with project objectives
- Assessment of in-scope assets, creation of requirements, and support of the public tender process through the selection of a provider
- Creation and maintenance of UML sequence diagrams to document system interactions
- Extensive use of Agile/Scrum methodology and Spotify model
- Development and implementation of cutover and dress rehearsal plans together with the application managers
Ralitsa P.
Last position:
Process, Project and Requirements Manager, Customer 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
- Management of the development team according to Scrum
- Design and project management of the API interface to existing tools
- Process modeling for the new Order Dispatching team (FCD & BD)
- Design of processes within and outside the team
- Requirements design and implementation of the necessary tools
- Creation and calculation of production KPIs
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.
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
19 years (Germany: 22 years)
Position duration
2.5 years (Germany: 2.6 years)
Positions per freelancer
16 (Germany: 14)
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Banking and Finance, Professional Services
Certification focus areas
Business Intelligence, Information Technology, Project Management
Bachelor's degree or higher
100% (Germany: 95%)
Master's degree or higher
63% (Germany: 77%)
Doctorate
13% (Germany: 19%)
Certifications per freelancer
6 (Germany: 3)
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Frankfurt 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 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 29 Oct 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 (63%)
- Professional Services (63%)
- Automotive (38%)
- Education (38%)
- Energy (38%)
- Healthcare (38%)
- Insurance (38%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Data Mining does
Data Mining identifies useful patterns, relationships and anomalies in large datasets. Specialists combine statistics, machine learning and database methods to support forecasting, segmentation, fraud detection and operational decisions. The work can cover structured records, text, logs, transactions and sensor data.
Typical applications
Data Mining appears wherever organisations need evidence from complex information:
- Customer segmentation and churn analysis
- Fraud, risk and anomaly detection
- Demand forecasting and recommendation systems
- Quality monitoring for industrial and digital processes
- Text classification and document analysis
Tools and ecosystem
Strong specialists work across SQL databases, data warehouses and distributed processing environments. Common tools include Python, R, pandas, scikit-learn, Spark and Jupyter, alongside visualisation systems such as Tableau or Power BI. They also understand APIs, cloud storage, orchestration and version control, so analytical work can move into reliable production workflows.
When companies need expertise
Companies often bring in freelance Data Mining expertise when internal teams have valuable data but lack the capacity to examine it rigorously. Support is useful during discovery, model selection, pipeline design, validation or the handover of a working solution. In Frankfurt, specialists may contribute remotely or work with local teams in workshops, subject to the project’s collaboration and language needs.
Deliverables that matter
A clear engagement should produce more than an interesting model. Typical deliverables include a data assessment, cleaned datasets, reproducible analysis, feature definitions, validated models, visual reports and practical recommendations. Strong documentation explains assumptions, limitations, data quality issues and how results should be monitored after release.
Signs of a strong specialist
The best professionals connect technical analysis with a company’s real decision process. Look for experience with relevant data types, careful validation and transparent communication about uncertainty. A dependable specialist can also:
- Translate business questions into measurable analytical tasks
- Detect leakage, bias, missing values and misleading correlations
- Compare methods against a meaningful baseline
- Protect sensitive information throughout the workflow
- Explain findings clearly to technical and non-technical stakeholders
Frequently asked questions
Key details about Data Mining, drawn from the questions we get asked most.
Data Mining is used to discover patterns and relationships in data that can guide business decisions. Typical applications include customer segmentation, fraud detection, demand forecasting, recommendations, risk analysis and process optimisation.
Data Mining looks for hidden patterns and predictive signals, while reporting usually describes what has already happened. Business intelligence tools can present and explore results, but mining specialists often create the analytical methods that reveal which factors matter and what may happen next.
A strong Data Mining specialist often combines SQL, statistics, Python or R, data preparation and machine learning. Experience with cloud warehouses, Spark, visualisation, APIs and model deployment can also be important when insights must reach operational systems.
The right level depends on data quality, business risk and the intended deliverable. A focused analysis may need a specialist who can work independently through exploration and validation, while a production system requires deeper knowledge of pipelines, monitoring, security and stakeholder adoption.
Data Mining is well suited to remote collaboration because datasets, notebooks, repositories and dashboards can be shared securely. Frankfurt teams may still prefer on-site workshops for discovery or handover, so agree on meeting cadence, access controls and German or English communication needs before work starts.
Ask how the specialist defines the business question, checks data quality and prevents leakage or overfitting. Good Data Mining work uses appropriate validation, compares results with a sensible baseline and explains uncertainty instead of presenting correlations as proof.
Data Mining is useful when relationships are too complex, numerous or changing for fixed rules and basic dashboards. Simple methods may be preferable when the decision is stable, the data is limited or transparency and ease of maintenance matter more than predictive power.
Before starting Data Mining, clarify the decision the analysis must improve, the available data, access permissions and the definition of success. Also confirm delivery expectations, reproducibility, ownership of notebooks or models, deployment responsibility and how results will be maintained after handover.
The average hourly rate of freelancers in Frankfurt, Germany who have used Data Mining in their recent projects is 100 €, which corresponds to a daily rate of about 803 € 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, 63% hold at least a Master's degree, and 13% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used Data Mining in their recent projects have 19 years of professional experience, with a single engagement typically lasting around 2.5 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 French (25%).
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 (63%), and Professional Services (63%).
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 (88%), and Product Development (75%).
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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