Data Scientists are needed.

Strong applied mathematical and statistical skills regardless of the tools.
Excellent written and verbal communication skills for coordinating across teams.
Knowledge of a variety of machine learning techniques (clustering, decision tree
learning, artificial neural networks, etc.) and their real-world advantages/drawbacks
0-3 years in the field of information and business intelligence systems.
Data-oriented personality.
Good applied statistics skills, such as distributions, statistical testing, regression, etc.
Good understanding of data mart schemas, and OLAP tools.
Good understanding of data visualization concepts and tools.
Good understanding of using query languages such as SQL.
Broad understanding of databases (e.g. Oracle PL/SQL, Mongo), and high-
the performance or distributed processing (e.g. Hadoop, Spark)
Deep understanding of probability, statistics and machine learning theory.
Experience with Python, Machine learning libraries, and data mining.
Good scripting and programming skills Python, R Data-oriented personality.
Must have a good understanding of machine/deep learning techniques and algorithms
(SVM, Naive Bayes, Decision Forests, Neural Networks, etc.)
Applied experience with machine learning on large datasets.
Demonstrated skills in selecting the right statistical tools given a data analysis
problem.
Experience with statistical software (e.g., R, Julia, MATLAB, pandas) and database
languages (e.g., SQL).
Hands on experience with machine learning frameworks such as TensorFlow, Keras,
etc... and techniques such as supervised machine learning, decision trees, logistic
regression etc...
Advanced level in Microsoft Excel
Build, test and deliver new analytics/models using the new data sets as required to
underpin use case.
Excellent understanding of machine learning techniques and algorithms such as k-
NN, Naive Bayes, SVM, Decision Forests, etc. Experience with common data science
toolkits.