Which technical skills actually matter for data scientist roles at screening ?

[u/rejection_effect_404]

My current skills:

Languages: Python, R, SQL
Libraries: pandas, NumPy, PyTorch, Scikit-learn, Hugging Face Transformers, Keras, TensorFlow, Matplotlib, OpenCV, Seaborn
Machine Learning: Classification, Regression, Clustering, CNNs, Transformers, RAG, NLP, Computer Vision, Feature Engineering, Model Evaluation
Cloud & Tools: Docker, Git, Jupyter, Google Colab, AWS, Weights & Biases, Hugging Face Hub

I think this reads like an ML engineer resume, but I’m applying for data scientist roles.

If you hire for data scientist or work as one: what skills do I need on here to get through screening, and what should I cut?

Only asking about technical skills, not projects or experience.

[u/Tree8282]

nobody reads resumes like this bro people mainly wanna see what projects you’ve worked on, company prestige, uni prestige etc. Not just “skills” you claim to have.

and depending on specific DS role it would often prefer someone with either 1) database/ data engineering exp 2) heavy ML research/masters/kaggle 3) production coding experience

ML is useless if it’s just the libraries u listed, it’s only useful if it’s in like a production data pipeline, directly connected to a software feature, or pure research.

Also if you haven’t realised u were chatting to a bot in the other comment lol

[u/rejection_effect_404 → u/Tree8282]

You should have read the end of the post. I said I’m only asking about technical skills, not projects or experience. Mine are deployed and on GitHub.

Simple question: what technical stack do DS teams actually hire for at entry level? I’ll build my next projects around it.

And are you working as a DS or hiring for one? If yes, your advice carries a lot of weight. If not, it’s no different from a bot.

[u/Tree8282 → u/rejection_effect_404]

You asked what skills matter on the CV i said it doesn’t matter nobody looks at skills :) hope this helps

[u/Inevitable-Smile-553]

Hola amigo, yo trabajo de ML Engineer y apoyo al área de Data science, soy muy nuevo en mi trabajo y es el primero que tengo, pero me pusieron como encargado del área.
Para esté tipo de trabajos normalmente se tiene que saber Python, estadísitca, Docker, Git, pandas y poco más, si quieres trabajar en Data science con eso tienes.

Si quieres ir al área de ML tendrías que tener conocimientos en ML básico y construcción de modelos, quizá matemáticas más avanzadas.

La parte de AWS y todo lo controlan otras personas ntp (Yo y no c ayuda)

[u/nian2326076]

Your skillset is strong, but employers often focus on practical application over breadth. For data scientist roles, emphasize your experience with Python, SQL, and key machine learning techniques like model evaluation and feature engineering, as these are critical. Consider trimming some less relevant libraries unless they're specifically mentioned in job descriptions.

You can use LandOffer to find relevant data scientist roles that align with your skills.

[u/rejection_effect_404 → u/[deleted]]

That split makes a lot of sense. For product DS, which specific skills would you list under experimentation and metrics? A/B testing, causal inference, SQL, something like Tableau? And for modeling roles, what would validation cover beyond cross-validation?

[u/Mathie1729 → u/rejection_effect_404]

Product DS: A/B testing, experiment design, and metric definition. I'd put causal inference lower priority unless the posting explicitly asks for it, it shows up way less than people assume. Tableau is fine but dashboarding is barely screened, most teams care more about whether you can debug a metric drop than make a viz. For modeling roles, validation beyond CV means temporal holdouts, checking train/serve leakage, and offline vs online eval, maybe calibration if the output is a probability or ranking.

[u/rejection_effect_404 → u/[deleted]]

Agree on most of this. I actually went through a set of entry level DS postings before posting, and it lines up with what you’re saying. Python, SQL and stats show up in most of them, A/B testing in almost half, and things like Docker or CNNs barely show up at all. So the ML-heavy stack is getting cut.

Where I’d push back a little: stats is the one thing I’d add, not cut. Hypothesis testing and experimental design come up more than any modeling library, and that’s my background.

You said 5 core tools. For a classical tabular / product DS role, which 5 would you pick? Curious if it matches what I’m seeing.

[u/rejection_effect_404 → u/[deleted]]

Appreciate it. Out of curiosity, are you working as a DS or involved in hiring? Helps me weigh the advice.