Data Scientist / ML Engineer
From statistics and clean data to models in production: turn data into decisions and ship ML that holds up in the real world.
✦ Guide me on this path with AIRoadmap progress
0% 0 of 7 steps done
How to read the signals
Importance High Market demand Medium Automation risk Low
- 1
Python + statistics
NumPy, pandas, probability and inference. Stats is the real foundation.
Importance High Market demand High Automation risk Low - 2
Data wrangling & EDA
Clean messy data and explore it before modeling. 80% of the job.
Importance High Market demand High Automation risk Medium - 3
SQL & databases
Joins, window functions, aggregations. Data lives in databases.
Importance High Market demand High Automation risk Low - 4
ML foundations
Regression, trees, validation, metrics, overfitting. Know why, not just fit().
Importance High Market demand High Automation risk Low - 5
Deep learning
Neural nets, PyTorch, transfer learning for vision/NLP when warranted.
Importance Medium Market demand High Automation risk Medium - 6
MLOps & deployment
Serve models behind APIs, track experiments, monitor drift. Shipping > notebooks.
Importance High Market demand High Automation risk Low - 7
Applied LLMs
Embeddings, RAG and evals — the data role now overlaps with AI engineering.
Importance High Market demand High Automation risk Low