---
esco_uri: http://data.europa.eu/esco/skill/d38871eb-b988-495e-8fca-345677b597a8
esco_label: "state estimation"
relation: optional
onet_soc: 15-2051.00
source: model-knowledge
confidence: high
qa_count: 0
generator: gemma3:27b (prompt-designed and spot-checked by Claude)
generated: 2026-07-10
---

# state estimation — Data Scientist

As a Data Scientist, 'state estimation' isn’t *always* about energy systems directly (despite the ESCO definition!). It translates into understanding the ‘true’ value of variables you *don't* observe directly, based on related data and models. Think fraud detection – you don’t see if someone is committing fraud instantly, but you estimate the probability based on transaction patterns, location, time, etc. Or in marketing attribution: you can't perfectly track which ad led to a sale, so you *estimate* contribution using statistical models (Markov Chains, Shapley Values) and observed conversions. It’s about inferring hidden states from noisy or incomplete data.

Typical tasks involve building and evaluating these estimation models. This means feature engineering to capture relevant signals, choosing appropriate algorithms (Kalman Filters are a direct link to the ESCO definition but regression, Bayesian networks, even deep learning can be used), and rigorously validating performance – not just accuracy, but calibration (are your confidence levels trustworthy?). Tools frequently include Python with libraries like `statsmodels`, `scikit-learn`, `pymc3` or `tensorflow/pytorch` for model building, plus data pipelines using tools like Spark or cloud services to handle large datasets. 

A common pitfall is overconfidence in the model’s output. State estimation *always* involves uncertainty. Ignoring this – presenting a single ‘predicted’ value without quantifying its range – can lead to bad decisions. Another issue is relying on flawed models; garbage in, garbage out applies heavily here. Careful feature selection and model validation are crucial. Also, be wary of assuming stationarity - real-world systems change over time, requiring adaptive or re-trained models.

Good state estimation looks like providing *actionable* insights with quantified uncertainty. It’s not just ‘this customer is likely to churn’, but ‘this customer has a 75% probability of churning within the next month, based on this data and model’.  It's about building systems that can adapt to new information and provide reliable estimates even in dynamic environments – ultimately helping stakeholders make informed decisions despite incomplete knowledge.

## Weitere Anreicherung

Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).
