Time Series Analysis Experts in Berlin
in minutes from over 15,000 CVs with vetted, available specialistsHire experts who turn timestamped data into forecasts, anomaly detection, and clear seasonal insights. They work with ARIMA, SARIMA, Prophet, decomposition, and production reporting pipelines. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Berlin, who have recently used Time Series Analysis
Mathias Wilhelm
Last position:
Implementation of an on-premise OCR solution with information extraction at Mindhopper GmbH
- Insurance service provider*
Challenge: Business-critical documents were processed through external OCR providers, with ongoing costs, dependency, and data privacy risks for sensitive insurance data.
Implementation:
- Architecture and production implementation of an on-premise OCR solution with full data ownership
- Methods for recognizing document structures as the basis for automated further processing
- ML-, NLP-, and LLM/VLM-based information extraction, especially from invoices and quotations
Success: Replaced external providers: full data ownership, GDPR-compliant processing, and 75% lower recurring OCR costs per year
Used technologies: Python, Docker, Microservices, FastAPI, PyTorch, Torchvision, MongoDB, MySQL
Nino Sandmeier
Last position:
Freelancer in Data Science at International Companies
Proceeding what was started in 10/2023, offering data science development skills fulltime to international clients
Helping companies learn more about their existing (unstructured) data, optimize processes and technical systems, and derive solutions for their problems
Tools and technology used: Python (sklearn, pandas, numpy, Django, sqlAlchemy, pyTorch), Matlab, Docker, AWS EC2, Lambda, S3, SQL, MySQL, Hadoop & Spark, Machine Learning, DNN, AI, Jira, Confluence, Git, CI/CD, GitLab, Jenkins
Sebastian Striebig
Last position:
Group Product Manager – Digital Platform Discovery at SPREAD.AI
- Developed and implemented organization-wide discovery framework based on Ulwick’s Outcome-Driven Innovation; enabled 7 Product Owners to systematically identify and quantify unrealized value through shared outcome language and opportunity scoring methodology
- Transformed Product Owner role from backlog clerks to strategic experimenters; established dedicated time budget for autonomous hypothesis testing and discovery activities
- Rebuilt customer journey maps to start at actual user need (tool selection phase) instead of platform entry point; eliminated manual data aggregation work previously done by project teams
- Implemented OKR framework across 4 product teams; defined quarterly objectives with measurable key results (e.g., 40% reduction in manual integration effort, self-service adoption increase)
- Unified 3 separate platform roadmaps through cross-team dependency mapping and shared service agreements
- Supported enterprise sales cycle with ROI modeling and technical due diligence for automotive and defense customers
Vili Dhamo
Last position:
Technical Lead, Data Engineer at Mercedes-Benz Consulting
- Optimized the data architecture (medallion) to better decouple processing stages and improve transparency and reproducibility
- Ensured technical quality of data processing in Databricks by introducing schema enforcement, data quality checks and a structured data architecture
- Orchestrated pipelines with Azure Data Factory
- Professionalized and automated the development and deployment process by integrating Git and GitHub Actions
- Led the Data Engineering team (3 members) in a functional role
- Conducted workshops to optimize and stabilize the data platform and the development process
- Collected and prioritized new requests, maintained the product backlog
- Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS SharePoint, Mural
Nooshin Omranian
Last position:
Senior Computational Biologist at Max-Planck-Institute for Molecular Genetics
- Conducting research at the interface of proteomics and artificial intelligence, focusing on the application of machine learning models (e.g., neural networks, clustering algorithms, and feature extraction) to analyze complex biological datasets.
- Developing and teaching AI-based analytical workflows for molecular and proteomic data, integrating tools such as Python (scikit-learn, TensorFlow, Pandas) for predictive modeling and data visualization.
- Collaborating with interdisciplinary teams to explore data-driven hypotheses in molecular genetics and enhance biological interpretation through AI-assisted pattern recognition.
- Implementing automated data processing pipelines to improve reproducibility and FAIR data management in high-throughput experiments.
Phil Howson
Last position:
Data Analyst at Applied Analytics Projects
- Designed and implemented end-to-end data workflows (BigQuery + PowerBI), transforming raw datasets into executive dashboards used for KPI monitoring
- Queried and transformed large-scale datasets using Google BigQuery to support analytical use cases and insight generation
Meisam Ghafarlangroudi
Last position:
Senior AI Engineer / Data Scientist at Geeks Ltd (WordUp)
Geeks Ltd is a UK-based technology company; WordUp is its AI-driven language-learning product focused on personalized vocabulary learning and intelligent educational experiences.
- Coordinate AI product delivery across Product, Engineering, Data, Operations, and leadership, translating user needs into scoped initiatives, sequencing work, surfacing blockers, facilitating hand-offs, and communicating progress.
- Own search, recommendation, retrieval, and content-enrichment features end to end, from requirements and architecture through Python/FastAPI implementation, testing, deployment, monitoring, and rapid iteration.
- Developed low-latency retrieval, ranking, and personalization services using AWS, OpenSearch, DynamoDB, embeddings, and reusable APIs, achieving <1s latency, 22% higher engagement, and 12% higher premium conversion.
- Use AI coding assistants for codebase analysis, scaffolding, refactoring, tests, debugging, and documentation while reviewing every output for correctness, architectural fit, security, maintainability, and user value.
- Represent technical work in planning and stakeholder discussions, gather requirements first-hand, challenge priorities constructively, explain delivery trade-offs, and help teammates make outcome-focused decisions.
Felix Brunner
Last position:
Project at Machine status detection in industrial 3D printing based on infrared image data
- Guided systematic data collection and pre-processing for the machine learning algorithms
- Defined the labeling process and implemented an interface to annotate the datasets
- Programmed a visual deep learning algorithm to detect machine pollution in live production
- Implemented data augmentation techniques to deal with machine heterogeneity
- Supplied a containerized model with API endpoints for deployment to the production machines
- Coordinated and represented a five-person project team, prepared presentations and reports
Subodh Khanger
Last position:
Student Assistant at University of Indore
- Collaborated on a pivotal project involving Quasi-Monte Carlo (QMC) methods for Bayesian optimization in PDEs, contributing to the implementation and focusing on uncertainty quantification.
- Addressed Bayesian inverse problems governed by PDEs, conducting detailed analysis for double integration problems using two approaches: a full tensor product and a sparse tensor product.
- Improved computational efficiency by developing and optimizing QMC-based algorithms, resulting in significant enhancements in the robustness of uncertainty quantification processes.
- Conducted extensive optimization and validation of algorithms, leading to more reliable and precise predictions in PDE models.
Karthikeyan A
Last position:
Cryptocurrency Price Prediction using Machine Learning Algorithms
- Designed, implemented, and evaluated multiple machine learning models (e.g., regression, time series, neural networks) to forecast cryptocurrency prices, incorporating data preprocessing, feature engineering, and model optimization for improved predictive accuracy.
- Performed in-depth data exploration and visualization on large cryptocurrency datasets, using tools like Python and libraries such as Pandas and Matplotlib to identify trends and patterns.
Gönenç Onay
Last position:
Freelance Data Analyst at D4C-Ai
Discover over 15,000 top freelancers
Statistics of experts using Time Series Analysis
Aggregated from the professional profiles of matched freelancers.
Experience
13 years
Position duration
1.8 years (Germany: 2 years)
Positions per freelancer
8
Top business areas
Information Technology, Business Intelligence, Research and Development
Top industries
Information Technology, Education, Banking and Finance
Certification focus areas
Business Intelligence, Information Technology, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
100% (Germany: 93%)
Doctorate
64% (Germany: 35%)
Certifications per freelancer
1 (Germany: 2)
Most common languages
German, English, Spanish
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 Berlin 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 Berlin using Time Series 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 it covers
Time Series Analysis studies data points ordered by time. It is used to forecast demand, spot anomalies, measure seasonality, and understand trends in business, operations, finance, IoT, and product data. Strong work here connects the math to a decision people can use.
Common methods
- Trend and seasonal decomposition
- ARIMA and SARIMA modeling
- Exponential smoothing and ETS
- Forecast validation and backtesting
- Anomaly and change-point detection
Tools and stacks
Professionals often work in Python, R, and SQL, with libraries such as statsmodels, pandas, Prophet, and scikit-learn. They also handle notebooks, reproducible scripts, and reporting layers that let teams inspect assumptions, residuals, and forecast quality.
When to bring in help
Companies bring in freelance expertise when forecasts are unreliable, patterns are hard to explain, or a model needs to move from analysis into regular use. In Berlin, that often means working with teams in e-commerce, logistics, mobility, energy, and SaaS that need clear time-based insight without long hiring cycles.
What strong specialists do
A strong specialist checks data frequency, missing values, outliers, and leakage before modeling. They explain why one approach beats another, keep the evaluation honest, and document how the model behaves across holidays, promotions, and other recurring events.
Delivery and collaboration
Time Series Analysis work often ends in a forecast, a monitoring workflow, a dashboard, or a decision rule for alerts and planning. Remote collaboration works well when data access and business context are clear, while on-site sessions help align assumptions, stakeholders, and release timing.
Frequently asked questions
Not sure where to start with Time Series Analysis? These answers cover the essentials.
Time Series Analysis is used to forecast future values, detect unusual changes, and understand recurring patterns over time. Companies use it for demand planning, capacity planning, sensor monitoring, financial signal review, and product metrics. The best work turns a noisy data stream into a decision that teams can act on.
Time Series Analysis focuses on order, lag, seasonality, and dependency on past values. General machine learning can work with time-based features, but it often misses the structure that makes timestamps special. In many projects, time-series methods are the better fit for forecasting and anomaly detection because they respect chronology.
A strong Time Series Analysis specialist often uses Python or R with tools like statsmodels, pandas, Prophet, and scikit-learn. Common methods include ARIMA, SARIMA, exponential smoothing, decomposition, and backtesting. The right mix depends on data quality, seasonality, and how explainable the result needs to be.
Not every Time Series Analysis task needs deep specialization, but messy data or business-critical forecasts usually do. If you need proper validation, multiple seasonal patterns, or deployment-ready logic, an experienced specialist is worth it. Simple trend reporting can be handled with lighter support.
For Time Series Analysis, ask how the person handles missing values, outliers, and leakage, and how they validate forecasts. Also ask what they would use for your data: ARIMA, SARIMA, ETS, Prophet, or another approach. Good answers should be specific to your data frequency and business goal.
A good Time Series Analysis deliverable is explainable, tested on past data, and tied to a real use case. Look for clear backtesting, documented assumptions, sensible handling of seasonality, and a forecast or alerting rule that the team can maintain. Quality is not just model fit; it is whether the result helps decisions.
Yes, Time Series Analysis can be done very well remotely because the main inputs are data, context, and feedback. In Berlin, some teams prefer a short on-site start to align on business rules, then continue online. What matters most is access to data, clear ownership, and quick review cycles.
A strong Time Series Analysis freelancer often brings SQL, Python, data cleaning, dashboarding, and basic statistics. In some projects, cloud data tools, forecasting pipelines, or monitoring setup also matter. Those adjacent skills help move the work from an analysis notebook into a useful process.
The average hourly rate of freelancers in Berlin, Germany who have used Time Series Analysis in their recent projects is 82 €, which corresponds to a daily rate of about 658 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Time Series Analysis in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 64% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Time Series Analysis in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Berlin, Germany who have used Time Series Analysis in their recent projects are German (100%), English (100%), and Spanish (18%).
The most common industries among freelancers in Berlin, Germany who have used Time Series Analysis in their recent projects are Information Technology (82%), Education (73%), and Banking and Finance (45%).
The most common business areas among freelancers in Berlin, Germany who have used Time Series Analysis in their recent projects are Information Technology (91%), Business Intelligence (82%), and Research and Development (82%).
Main locations of FRATCH Experts, who have recently used Time Series 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 
Munich Request a free demo
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Philipp Thomaschewski
FRATCH CEO
