KNOWLEDGE, SKILLS AND ABILITIES Knowledge of: 2 yrs of academic + 4 yrs of professional experience. I don’t claim to be a mentor/coach nor do I claim myself to having an extraordinary track record. Although, whatever I am putting down in this blogpost is a result of practical experience that I have over interviewing 100+ profiles in the ML domain in last 2–3 years. Skills in: ML algorithms, modeling and feature engineering ML has become the most visible aspect of the modern data scientist's job as it requires them to build models from data using their skills for machine learning methods and algorithms. Data scientists need to understand the vast range of ML algorithms including: Decision trees, Random forests, Bagged and Boosted tree Approaches; Bayesian methods; K-nearest neighbors; Support vector machines; Ensemble methods; Clustering approaches including k-means, gaussian mixture and principal component analysis; Markov models; and recurrent neural networks, convolutional neural networks and Boltzmann machines. Ability to: Histograms; Bar and area charts, pie and line charts, waterfall charts, thermometer and candlestick charts; Segmentation and clustering diagrams; Scatter plots and bubble charts; Visualizations of classification space; Methods for visualization during exploratory data analysis; Frame and tree diagrams; Funnel charts, word clouds, heatmaps, video and image annotations; Map and geospatial visualizations; and the use of a wide range of gauges, metrics and measures.