Time Series Analysis Experts in Berlin
matched in minutes with the power of AIHire experts who uncover temporal trends, forecast demand, and build resilient anomaly detection pipelines, precisely matched through our platform of vetted, available talent.
Meet FRATCH Experts in Berlin, who have recently used Time Series Analysis
William N.
Last position:
Power BI Solutions Architect/Engineer & AI Consultant at AVERDUNG GmbH
- Redesign of the company’s BI infrastructure: replacement of a fragmented landscape of manually maintained Excel solutions and CSV imports with a centralized Power BI environment using a unified data model as the company-wide single source of truth
- Consolidation of previously isolated reporting logic in a central semantic model – elimination of redundant files, manual data transfers and inconsistent metrics across departments
- Forecasting & Planning: Design and implementation of company-wide liquidity planning in Power BI – from business logic to a fully automated, data-source-driven planning model replacing the previous manual Excel process; enabling rolling forecasts and continuously up-to-date cash flow transparency for management
- Optimization of existing Power BI dashboards in terms of performance, structure and analytical value using an AI-native approach
- Analysis and improvement of the data model, including data quality analyses, data cleansing and consistent modeling using Star Schema, DAX and Power Query
- Incident & Anomaly Analysis: Identification, investigation and explanation of data anomalies, including root-cause analysis and specific recommendations for action
- AI Solution Architecture: Connecting Business Central and Power BI to LangDock via MCP (Model Context Protocol) for AI-supported data usage
- Development of a historical data layer as a basis for trend and time-series analyses
- AI-supported automation: Design and development of AI skills, agents, loops and processes for the automated analysis and interpretation of reports
- Automated reporting workflow: Setup of scheduled, automated email delivery of AI-generated analyses and recommendations to stakeholders
- Gathering and documentation of business requirements and coordination with business departments and IT as part of requirements engineering / product owner activities
- Breaking down overall requirements into clearly defined work packages and tasks
- Definition, prioritization and management of milestones throughout the entire project lifecycle
Tools: POWER BI, M365, Copilot Studio, MIRO, Microsoft Business Central, Microsoft Fabric, Claude AI, ChatGPT, LangDock, MS VS Code
Vili D.
Last position:
Technical Lead, Data Engineer at Mercedes-Benz Consulting
Optimized the data architecture (Medallion) to better decouple processing steps and improve transparency and reproducibility
Ensured the technical quality of data processing in Databricks by introducing schema enforcement, data quality checks, and a structured data architecture
Pipeline orchestration with Azure Data Factory
Improved and automated the development and deployment process by integrating Git and GitHub Actions
Technical leadership of the Data Engineering team (3 employees)
Led workshops to optimize and stabilize the data platform and development process
Collected and prioritized new requests and 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
Özlem A.
Last position:
Adjunct Lecturer at Whitecliffe University of Applied Sciences
- Teaching Introduction to Data Science Course in MBA Programme
Mathias W.
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
Sebastian S.
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
Nino S.
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
Nooshin O.
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 H.
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
Niowsha F.
Last position:
Machine Learning Research Assistant (HiWi) at DIGIT
- Train and optimize MAVAE/VAE models in PyTorch to detect anomalies in multivariate time-series sensor data.
- Design preprocessing workflows and evaluation pipelines to improve model accuracy and robustness.
- Benchmark MAVAE performance against baseline statistical and deep learning approaches, presenting comparative insights.
- Collaborate with research supervisors to refine hypotheses and translate experimental findings into deployable research outputs.
Felix B.
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
Meisam G.
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.
Mark W.
Last position:
IT Manager at WESSLING Consulting Engineering
- Overall responsibility for IT strategy and IT operations, reporting directly to the executive management.
- Implementation of business requirements as production-ready solutions within a few days, supported by practical data science experience and expertise from the energy sector.
- Led the IT carve-out of a medium-sized company: separation of infrastructure, systems, and contracts into an independent IT organization.
- End-to-end responsibility for IT infrastructure: hardware procurement, cloud services (including GPU resources), and networking.
- Selected and introduced a new ERP system together with the executive management.
- Managed external MSPs and service providers, including contract, SLA, and license management.
- Responsible for IT security, GDPR compliance, backup, and business continuity; provided IT support for ISO 9001 certification.
- IT budget planning and cost optimization.
Subodh K.
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.
Mirage F.
Last position:
Senior Business Intelligence Analyst at Berliner Verkehrsbetriebe (BVG)
- Own and develop Power BI reporting for digital sales channels, subscriptions, mobility products and the operational performance of a large public transport company.
- Create reusable semantic models, DAX measures, Power Query transformations and KPI definitions for monthly management reporting and interactive decision support.
- Translate sales and operational questions into scalable dashboards covering sales performance, customer behavior, product usage and service processes.
- Support the migration of analytical workloads from Azure Synapse to Databricks SQL, including source validation, view design and data quality checks.
- Integrate web and app analytics as well as subscription, CRM, consent, customer service and transaction data into consistent reporting processes.
- Work with Product Owners, Data Engineers and stakeholders from Marketing, Sales and Governance to improve data usability, documentation and trusted self-service analytics.
Gönenç O.
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
1.9 years
Positions per freelancer
8
Top business areas
Business Intelligence, Information Technology, Research and Development
Top industries
Information Technology, Education, Professional Services
Certification focus areas
Business Intelligence, Information Technology, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
100% (Germany: 94%)
Doctorate
53% (Germany: 34%)
Certifications per freelancer
3
Most common languages
German, English, Spanish
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 Berlin 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 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 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Time Series Analysis 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 (80%)
- Education (73%)
- Professional Services (53%)
- Banking and Finance (47%)
- Automotive (33%)
- Manufacturing (27%)
- Energy (20%)
- Retail (20%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Temporal Data and Forecasting Systems
Time series analysis extracts meaningful statistics and patterns from data indexed in time order. Organizations use these methods to understand historical behavior, isolate seasonal variations, and project future metrics. Practical applications include automated capacity planning, financial risk modeling, predictive maintenance, and real-time fraud monitoring across high-throughput streaming architectures.
Core Methods and Framework Ecosystem
Work in this discipline spans classical statistical modeling and modern deep learning architectures:
- Classical autoregressive models including ARIMA, SARIMAX, and state-space formulations
- Specialized forecasting toolkits like Prophet, Darts, and Kats
- Machine learning frameworks such as XGBoost and LightGBM using lagged features
- Deep sequence architectures including temporal fusion transformers and LSTMs
- Specialized time-oriented storage engines such as TimescaleDB and InfluxDB
Production Challenges and Pitfalls
Temporal data introduces risks that standard cross-sectional modeling does not face. Data leakage through lookahead bias invalidates evaluation metrics if split routines ignore sequential order. Non-stationarity, concept drift, missing intervals, and irregularly sampled observations require specialized backtesting schemes such as rolling-origin evaluation to ensure forecasts remain reliable under real conditions.
Typical Project Triggers
Companies typically bring in specialized talent when standard machine learning approaches fail on sequential metrics:
- Inventory stockouts or oversupply caused by poor demand forecasting
- Inability to flag equipment failures prior to critical downtime
- Alert fatigue generated by static threshold alerts on noisy telemetry
- Migration from batch predictive reporting to continuous real-time stream scoring
Application Across Berlin Tech Hubs
Berlin hosts dense clusters of fintech startups, e-commerce platforms, and logistics networks where temporal modeling is central. Local companies rely on temporal data for dynamic pricing algorithms, customer churn trajectory forecasting, and mobility fleet rebalancing. Teams in the region frequently operate in English while collaborating across distributed hybrid setups.
Hallmarks of Strong Domain Specialists
Distinguished professionals avoid applying complex neural architectures when classical parsimonious models yield superior generalization. They demonstrate meticulous validation strategies, handle regime changes gracefully, and communicate forecast uncertainty intervals clearly to business stakeholders rather than presenting point estimates as guarantees.
Frequently asked questions
Not sure where to start with Time Series Analysis? These answers cover the essentials.
Organizations apply time series analysis to predict future demand, detect operational anomalies, and track performance changes over time. Common deployments include dynamic energy pricing, server capacity planning, inventory management, and sensor anomaly detection on industrial Internet of Things devices.
Statistical techniques like ARIMA and exponential smoothing excel on univariate series with limited training points and pronounced seasonality. Deep learning architectures like temporal fusion transformers perform best when millions of interrelated series share underlying patterns, though simpler baselines often remain harder to beat.
Most specialists write Python or R code using libraries like statsmodels, Darts, and scikit-learn for modeling. When handling large-scale time series analysis workloads, they interact with specialized engines like InfluxDB, TimescaleDB, or distributed compute frameworks like Apache Spark.
Look for demonstrable familiarity with rolling-origin cross-validation, feature lag generation, and non-stationarity diagnostics. Proven time series analysis professionals know how to prevent data leakage and measure performance with business-aligned metrics rather than standard mean squared error alone.
Yes, nearly all time series forecasting development, distributed training, and batch scoring take place in cloud environments. Data pipelines and model registries integrate seamlessly into remote workflows without requiring on-premise hardware.
Berlin has an established pool of tech talent across e-commerce, mobility, and fintech where temporal modeling is routine. While time series analysis work is easily executed remotely, local specialists facilitate face-to-face workshops and align naturally with Central European working hours.
The technical community in Berlin operates predominantly in English across startups and scale-ups. A specialist conducting time series analysis rarely requires fluent German unless direct collaboration with public-sector systems or traditional local enterprises demands it.
Rigorous practitioners avoid randomized train-test splits and instead apply walk-forward validation strategies. When performing time series analysis, all feature transformations, scaling parameters, and target calculations must only ingest observations timestamped prior to the forecast horizon.
The average hourly rate of freelancers in Berlin, Germany who have used Time Series Analysis in their recent projects is 86 €, which corresponds to a daily rate of about 687 € 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 53% 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 1.9 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 (20%).
The most common industries among freelancers in Berlin, Germany who have used Time Series Analysis in their recent projects are Information Technology (80%), Education (73%), and Professional Services (53%).
The most common business areas among freelancers in Berlin, Germany who have used Time Series Analysis in their recent projects are Business Intelligence (87%), Information Technology (87%), and Research and Development (87%).
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 
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Philipp Thomaschewski
FRATCH CEO
