Despre acest rol
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Abilități/categorii
About data / ml roles
Data and ML roles split into three camps: analysts (SQL, dashboards, business questions), engineers (pipelines, infrastructure), and scientists (modeling, experimentation). The job title doesn't always tell you which one — read the description carefully.
Abilități tipice: SQL, Python, pandas, basic stats; ML roles add scikit-learn / PyTorch / TensorFlow; engineering roles add Airflow / dbt / Spark
Informații despre salariu (SUA, aproximativ)
Typical range for data / ml roles in the US is $85,000–$240,000/year, variind foarte mult în funcție de vechime, stadiul companiei și oraș.
Doar estimări. Pentru numerele specifice companiei, verificați levels.fyi (tech), Glassdoor sau întrebați în interviu.
Cum să vă pregătiți pentru interviu
Data interviews split: analysts get SQL + case-study questions, engineers get pipeline-design + coding, scientists get stats + ML modeling + sometimes a take-home dataset. Read the JD carefully and prepare to the actual flavor — don't waste time on PyTorch for an analyst role.
Common questions across all three: "How would you measure success for [product feature]?", "Walk me through a project where the data told you something surprising", și SQL window functions (LAG, ROW_NUMBER, PARTITION BY — practice these specifically). For ML roles, expect a question about overfitting, bias-variance tradeoff, and how you'd debug a model that performs well in training and badly in production.
Unde duce de obicei acest rol
Career paths in data/ML have gotten messier as the discipline has matured. The clearest progression: Junior Analyst → Senior Analyst → Analytics Manager on the analyst side, Junior Data Engineer → Senior → Staff on the engineering side, and Junior DS → Senior DS → Principal DS / ML Lead on the modeling side. Salary peaks are highest for ML engineers + research scientists at big-tech firms.
Cross-discipline moves are common: analysts who learn engineering often outearn engineers who didn't learn business context. Stay close to the business — the most valuable data people are the ones executives bring into strategy discussions, not the ones building dashboards alone.
Steaguri roșii de urmărit
- "Data scientist" role that's really a SQL analyst. Read the responsibilities carefully — if it's all dashboards and ad-hoc queries, it's an analyst role with a science title.
- No mention of stakeholder collaboration. Data work in isolation is usually data work that gets ignored. The role needs to be embedded with a business team.
- "AI / ML" listed everywhere but no specifics. If they can't name the actual problems they want to solve with ML, the company is buzzword-shopping.
- Asking for "10+ years of Python" or impossible combinations. Either the JD was written by HR without input from a data team, or the company has unrealistic expectations.
Întrebări frecvente
Cum aplic pentru acest rol?
Faceți clic pe butonul „Aplicați pe Arbeitnow” din partea de sus a acestei pagini. Veți fi trimis la postarea inițială unde angajatorul acceptă cererile. Wikishopline nu colectează CV-uri și nu procesează cereri.
Este această listă actuală?
Wikishopline acumulează locuri de muncă zilnic din surse partenere (arbeitnow). Postările mai vechi de ~14 zile sunt tăiate, dar verificați întotdeauna că rolul este încă deschis pe site-ul angajatorului înainte de a petrece timp cu o scrisoare de intenție.
Wikishopline taxează angajatorii sau solicitanții?
Nu. Lucrările agregate sunt gratuite pentru ambele părți. Wikishopline acceptă, de asemenea, postări plătite de 5 USD/30 de zile la /jobs/submit pentru angajatorii care doresc vizibilitate directă – dar lista pe care o vizualizați a fost obținută de la un partener.
What does a data / ml role typically involve?
Data and ML roles split into three camps: analysts (SQL, dashboards, business questions), engineers (pipelines, infrastructure), and scientists (modeling, experimentation). The job title doesn't always tell you which one — read the description carefully.
What's the typical salary range for data / ml roles in the US?
Roughly $85,000–$240,000 USD/year, depending on seniority, location, and company stage. Aceasta este o gamă largă intenționată — verificați cu levels.fyi sau Glassdoor pentru compania respectivă.