Data Scientist

Statistics Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators, etc. Proficiency in statistics is essential for data-driven companies.

Machine Learning good knowledge of machine learning methods like k-Nearest Neighbors, Naive Bayes, SVM, Decision Forests.

Strong Math Skills (Multivariable Calculus and Linear Algebra) - understanding the fundamentals of Multivariable Calculus and Linear Algebra is important as they form the basis of a lot of predictive performance or algorithm optimization techniques.

Data Wrangling proficiency in handling imperfections in data is an important aspect of a data scientist job description.

Experience with Data Visualization Tools like matplotlib, ggplot, d3.js., Tableau that help to visually encode data

Experience Required

.

1. Data processing and visualization are essential skills for generative AI projects, as they help you to prepare and analyze the data that you use to train and test your models.

2. Candidate should be able to perform data cleaning, manipulation, augmentation, and normalization using libraries like pandas, numpy, and scikit-learn. You should also be able to create and interpret data visualizations using libraries like matplotlib, seaborn, and plotly.

3. Data processing and visualization can help you to understand the patterns, trends, and features of your data, as well as to identify and debug any issues or errors in your models.

4. Candidate should also be able to use statistical methods and metrics to measure the quality and diversity of the generated outputs, such as mean squared error, KL divergence, inception score, and Fr chet inception distance.