Predictive Analytics Experts in Germany
matched in minutes from over 15,000 CVs with the power of AIHire experts who turn data into forecasts, risk scores, churn signals, and demand plans with predictive modeling, Python, SQL, and BI tools. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used Predictive Analytics
Daryoosh Dehestani
Last position:
FP&A Data & AI Architect at Epta Group
Scope: Embedded as FP&A Data & AI Architect within the Finance function of a major European refrigeration manufacturer, leading the transformation of manual, fragmented financial reporting into an automated, governance-driven intelligence platform. Driving the shift from Excel-based controlling to structured data architecture, Power BI analytics, and AI-assisted financial operations.
Financial Data Integrity & ERP Governance
- Initiated and led GL vs. subledger reconciliation investigations, identifying and resolving structural mismatches between General Ledger and subledger data that had gone undetected prior to engagement
- Conducted asset analysis to identify items missing from General Ledger postings, surfacing gaps in fixed asset tracking and period-end completeness
- Validated SAP reports, establishing baseline data quality standards for Finance team consumption
- Established systematic SAP data validation framework ensuring ongoing integrity between ERP postings and downstream reporting outputs
Finance Reporting Transformation
- Designed and implemented a structured Transformation Project approach for converting manual Finance reports into fully automated processes
- Created and owns the Data Reporting Audit Log; a centralized tracking system capturing report owners, stakeholders, data sources, manual effort estimates, and automation opportunity scores across the Finance function
- Mapped the full reporting landscape identifying quick-win automation targets and strategic Power BI migration candidates
- Actively reducing manual Excel and PowerPoint dependency across FP&A workflows; replacing point-in-time snapshots with live, governed data models
Power BI & Analytics Enablement
- Introduced and presented Power BI as the strategic reporting platform to Finance leadership, building internal buy-in for the BI transformation roadmap
- Designed initial Power BI architecture aligned with SAP, Salesforce and Oracle data structures and FP&A reporting requirements
- Established report ownership, governance documentation, and data lineage standards enabling sustainable self-service analytics across the Finance team
Transformation Infrastructure & Collaboration
- Configured and deployed Jira as the transformation project management hub, establishing structured sprint workflows, backlog management, and progress visibility for Finance IT initiatives
- Proposed and initiated a dedicated FP&A Communication & Transformation Hub, a structured cross-functional forum aligning Finance, IT, and business stakeholders around the reporting transformation roadmap
- Positioned the Finance function as an active driver of data governance and digital transformation within the broader organization
Outcomes
- GL/subledger reconciliation gaps identified and investigation framework established within first two weeks of engagement
- Data Reporting Audit Log deployed; first structured inventory of Finance reporting landscape in company history
- Power BI transformation roadmap presented and approved by Finance leadership
- Jira-based project governance live; Finance transformation now tracked with full sprint visibility
Technologies: SAP FI/CO · Power BI · DAX · SQL · Excel (advanced) · Power Query (M) · Power Automate · VBA · Jira · Microsoft 365 · SharePoint · Salesforce (Sales Data) · Oracle HCM · Python
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
Beshr Alnirabieh
Last position:
System Administrator – HealthCare IT & Data Infrastructure at Cellitinnen Hospital Association
- Integration of medical modalities (including ultrasound) into the existing IT infrastructure (DICOM, HL7) – put into operation within the planned timeframe.
- Administration and optimization of PACS systems for efficient archiving and distribution of radiology image data across multiple locations.
- Ensuring consistent data quality and seamless interoperability in data exchange between HIS, RIS, and PACS.
- Close collaboration with medical staff to analyze and digitally optimize clinical workflows.
- Requirements management and test coordination when implementing clinical requirements in complex IT structures.
Valery Khamenya
Last position:
Sr. Data Scientist & Engineer at Virtual Minds
- Development of high-performance ad distribution via auction
- Holistic (multi-campaign & multi-channel) advertisement placement optimization
- Algorithmic optimization for NP-Hard/NP-e
- Multiple Knapsack Problem with constraints
- Online estimation of parameters in stochastic environments
Tools: Python, R, Kotlin, MILP/SAT/CP Solvers, Pytorch, Pandas, Docker
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
Archana Ravikumar
Last position:
Power BI Assistant at Cataliquent Projekt GmbH
- Evaluate, design, and implement Power BI solutions for Cat4 reporting, significantly improving reporting efficiency, accuracy, and stakeholder visibility.
- Develop and maintain robust data integration interfaces between Cat4 and Power BI, eliminating manual processing bottlenecks.
- Configure and continuously optimise Power BI reports to deliver sharper management insights and support data-driven decision-making.
- Ensure full GDPR data protection compliance across Power BI reports, proactively identifying and mitigating risks.
- Acquired in-depth product knowledge of CAT4, a globally deployed enterprise platform, enabling effective requirements analysis, solution design, and seamless Power BI integration across client environments.
- Developed and integrated scalable reporting templates within the CAT4 product interface, delivering new product features and enhancing data visualisation, reporting efficiency, and stakeholder visibility across global users.
- Trained and mentored staff on Power BI configuration and best practices, accelerating adoption and embedding a data-driven culture.
- Prepared clear technical documentation and delivered hands-on demonstrations to end users and clients, ensuring smooth adoption and confident use of Power BI solutions integrated with CAT4.
- Researched and recommended licensing strategies for cost-effective and scalable Power BI deployment.
SKILLS - BI & Visualisation: Power BI (Desktop, Service, Report Builder), DAX, Power Query
Project and Product Management Tool: CAT4
Julia Sagert
Last position:
Senior Data Scientist / Consultant at Cloud Nation GmbH
Python, SQL, PySpark, Databricks, Databricks SQL, Delta Lake, dbt, Azure Data Lake Storage, Azure Machine Learning, Azure DevOps, Power BI, Git, MLflow
- Developed, validated, and optimized predictive analytics and classification models using Python (pandas), SQL, and modern ML frameworks.
- Performed data analysis, feature engineering, model validation, cross-validation, and stability analysis to ensure robust model quality and performance.
- Communicated model assumptions, results, uncertainties, and limitations to business units, management, and technical stakeholders.
- Built scalable data and machine learning workflows in cloud-based analytics environments using Databricks and Microsoft Azure.
Eric Bouendeu
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
Ashkan Zadeh
Last position:
Microsoft Azure Senior Data Engineer / Senior Data Scientist at Vattenfall Europe
- Advising on the use of analytics and BI tools and services in the Microsoft Azure stack (e.g. MS Fabric, Synapse Workspaces and dedicated SQL pools, SQL Database, PostgreSQL, Snowflake, Databricks, Data Factory, SSIS, Analysis Services, Function Apps, Power BI, ML)
- Independently designing analytics solutions with Python, SQL, etc.
- Designing and implementing ETLs and data pipelines
- Creating and maintaining APIs
- Independently applying CI/CD, testing, and version control
- Data modeling
- Model development and optimization
- Anomaly detection with AI
- Predictive analytics
Used technologies:
- Snowflake
- Fabric
- Azure Synapse Analytics
- Azure DataFactory
- Azure Data Lake
- Azure DevOps
- Databricks
- Spark
- CI/CD
- SQL Database
- Python
- Power Platform
Oliver Köhn
Last position:
Consultant for data-driven AI solutions at Oliver Köhn - IT-Freelancer
- AI-powered automation with a focus on efficiency, information processing, and assistant systems
- Automated email classification (OpenAI, FastAPI)
- Contract analysis for LegalTech (Llama 3, LangGraph)
- Internal knowledge search with RAG (VLLM, Hugging Face)
- Anomaly detection on edge devices (LLAVA, TensorRT)
- Agent system for management reports (LangGraph, Zapier)
Ulf Schiebener
Last position:
Innovation Manager at Claas
- Steer innovation projects for AI-based systems, focusing on plant detection technology for agricultural applications.
- Champion digital transformation initiatives within software departments, advocating for Agile methodologies and Scrum-based workflows.
- Analyze and optimize team infrastructure, fostering a culture of continuous improvement and strategic development.
- Pioneered integration of AI technology in agricultural equipment, significantly advancing precision farming techniques.
- Designed a cloud-based dashboard platform, facilitating real-time data access and decision-making for field management.
- Developed camera-based GPS systems for tractors, enhancing field analysis capabilities.
- Created and implemented AI interfaces for pre-processing image data, ensuring seamless statistics delivery to the cloud via LTE.
- Innovated cloud-based dashboard platforms, introducing cutting-edge technology concepts and coordinating cross-functional teams to execute project visions.
Christian Schulz
Last position:
Data-Scientist/AI Engineer at The Marcom Engine GmbH & Co. KG
- Concept creation and implementing AI Agents in AWS Cloud
- Continuously alignment with stakeholders
- Collaborate with DevOps
- Technologies: Git, CI/CD (GitHub Actions), Python/ML, Streamlit, Deno/typescript, AWS SAM, AWS Bedrock, AWS Lambda, AWS Dynamo DB, AWS S3, AWS Event Bridge etc.
Raghu Ram Vadali
Last position:
Telco Customer Churn Prediction – End-to-End ML Pipeline at Self-Initiated Project
- Designed and implemented a full machine learning pipeline for churn prediction using the Telco dataset.
- Applied preprocessing techniques including missing value handling, categorical encoding, feature scaling, and PCA.
- Built and compared over 15 models (logistic regression, random forest, XGBoost, etc.) and evaluated them using accuracy, precision, recall, F1 score, ROC AUC, and PR AUC.
- Tuned hyperparameters with GridSearchCV, achieving 80.6% accuracy with random forest and XGBoost.
- Created visual reports (bar plots, heatmaps, radar charts) to interpret model performance and churn drivers.
- Exported reusable pipelines and trained models with joblib for deployment.
Bhanu Prakash Avula
Last position:
CRM and MarTech Expert at Merkle DACH
- Configure and customize Salesforce Marketing Cloud, Braze, and Data Cloud to meet client-specific requirements, ensuring seamless deployment and scalability
- Develop and maintain custom APIs, automation workflows, SQL queries, Liquid Script, AMP script, and SSJS scripts for data processing, personalization, and dynamic content
- Design and implement multi-channel campaigns and customer journeys across email, SMS, push notifications, and in-app messaging
- Build and maintain integrations with CRM systems, analytics platforms, data warehouses, and third-party tools using REST/SOAP APIs and connectors
- Develop data models, ETL processes, and pipelines to synchronize data across systems, enabling a unified customer view and real-time engagement
- Optimize platform performance by implementing error handling, logging, and monitoring mechanisms
- Provide development support for campaign setup, deployment, and monitoring
- Optimize customer journeys and automation workflows using Journey Builder, Audience Builder, and Automation Studio
- Conduct A/B testing, performance analysis, and reporting to enhance campaign effectiveness and ensure maximum ROI
- Design and maintain data extensions, segmentation strategies, and audience targeting rules for effective customer communication
- Leverage Data Cloud capabilities to unify customer profiles, enable predictive analytics, and personalize customer interactions
- Act as a technical SME, providing support for complex issues related to platform configuration, integrations, and campaign execution
- Troubleshoot API integrations, scripting errors, automation failures, and data synchronization issues
- Collaborate with vendors and internal teams to resolve critical issues and deploy fixes
- Establish development standards, reusable templates, and best practices to ensure consistency and scalability
- Document technical designs, workflows, configurations, and troubleshooting guides
- Conduct training sessions and knowledge transfers to empower internal teams and clients
- Develop custom dashboards and performance reports to track campaign metrics and data trends
- Stay updated with Salesforce Marketing Cloud, Braze, and Data Cloud releases and industry trends
- Evaluate and implement new tools and AI-powered solutions for predictive analytics, segmentation, and personalization
Benedict Baur
Last position:
Reporting Application for Participation Information at Freelance
Development of ABAP CDS Views in S/4
Consumption via oData service by reporting tools like Power BI
Discover over 15,000 top freelancers
Statistics of experts using Predictive Analytics
Aggregated from the professional profiles of matched freelancers.
Experience
16 years
Position duration
2.2 years
Positions per freelancer
10
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Professional Services, Automotive
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
98%
Master's degree or higher
71%
Doctorate
20%
Certifications per freelancer
3
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 Predictive Analytics
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 it covers
Predictive analytics uses historical data to estimate future outcomes and likely behavior. It supports forecasting, scoring, classification, and early warning models for sales, operations, finance, and product teams. Strong specialists connect the model output to a decision, not just a dashboard.
Common work
- Demand and revenue forecasting
- Customer churn and lead scoring
- Fraud, risk, and anomaly detection
- Capacity planning and inventory signals
- Predictive dashboards for managers
Tools and methods
This work often combines Python, R, SQL, and notebooks with libraries such as scikit-learn, statsmodels, and XGBoost. Many teams also use Power BI, Tableau, or SAS for reporting and model delivery. Good experts know how to clean data, select features, validate results, and explain model limits in plain language.
When to bring in help
Companies usually bring in freelance expertise when a dataset is ready but the model path is unclear, or when an existing forecast is too weak for daily decisions. It also helps during audits, migration projects, and short sprints where internal teams need extra analytical depth. In Germany, this is common when local teams need remote support but still want clear communication and clean handover docs.
What strong specialists do
- Frame the business question before choosing a model
- Test assumptions and compare baseline approaches
- Watch for leakage, bias, and unstable features
- Translate results into actions for non-technical teams
- Document inputs, thresholds, and maintenance steps
Delivery and fit
Predictive analytics work is often delivered as a model prototype, scoring pipeline, forecasting logic, or reporting layer. The best experts can work with data engineers, analysts, and product owners without slowing the team down. For Germany-based projects, they should also handle remote collaboration well and adapt to English-speaking or mixed-language teams when needed.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Predictive Analytics.
Predictive Analytics is used to estimate what is likely to happen next, based on past and current data. Companies use it for forecasting, churn prediction, fraud detection, risk scoring, and planning. The output should support a decision, not sit in a slide deck.
Predictive Analytics looks forward, while BI and reporting mainly describe what already happened. A dashboard may show a sales drop; a predictive model helps estimate which customers are likely to leave or which regions may miss target. In practice, the two work best together.
Not exactly. Predictive Analytics is the broader discipline, while predictive modeling is one part of it. The work also includes data prep, feature design, validation, deployment, and making the results usable for teams.
A strong Predictive Analytics specialist usually knows Python, SQL, statistics, and model evaluation. Experience with scikit-learn, pandas, time series methods, and clear data storytelling helps a lot. For many projects, business understanding matters as much as the model itself.
You do not need a perfect data science setup before bringing in Predictive Analytics help. Freelancers are useful when the data is messy, the target variable is unclear, or a forecast needs to be trusted by business teams. The best time is often before the team commits to the wrong model path.
Yes, most Predictive Analytics work can be done remotely if access to data, stakeholders, and definitions is well organized. For Germany-based companies, that usually means clear written handover, predictable meetings, and good coordination with local teams. On-site time can help at the start of a project, but it is rarely required for the full delivery.
Look for a Predictive Analytics specialist who explains assumptions, compares a simple baseline to the final model, and shows how results are checked over time. Good work includes clean documentation, clear metrics, and a realistic view of model limits. If the answers stay vague, the project risk is usually high.
Teams often compare Predictive Analytics with rules-based logic, simple trend analysis, or more advanced machine learning methods. The right choice depends on data quality, explainability needs, and how the output will be used. A good freelancer will help decide whether the problem even needs a predictive model.
The average hourly rate of freelancers in Germany who have used Predictive Analytics in their recent projects is 101 €, which corresponds to a daily rate of about 808 € based on an 8-hour working day.
Of the freelancers in Germany who have used Predictive Analytics in their recent projects, 98% hold at least a Bachelor's degree, 71% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Germany who have used Predictive Analytics in their recent projects have 16 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 Predictive Analytics in their recent projects are German (100%), English (100%), and French (18%).
The most common industries among freelancers in Germany who have used Predictive Analytics in their recent projects are Information Technology (84%), Professional Services (49%), and Automotive (42%).
The most common business areas among freelancers in Germany who have used Predictive Analytics in their recent projects are Information Technology (96%), Business Intelligence (91%), and Product Development (67%).
Main locations of FRATCH Experts, who have recently used Predictive Analytics
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 
Munich Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
