Family Description

Analytics (AN) covers the promoting of data science to demystify big data and unlock new business potentials from complex data sources. This covers a huge scope: from classical data gathering and business intelligence, through designing and implementing machine learning, all the way to architecting systems that automate data gathering, analytics, and integrating their outcome into other applications. Determines the foundation for data driven and fact based decision making. Provides the tools and intelligence to formulate strategies and future business development plans.

Subfamily Description

Data Science & Analytics (DAS) consists of the interpretation and analysis of data using statistical techniques, visualisation, and predictive analytics. Covers the development and implementation of databases, data collection systems, data analysis, and other strategies that optimise statistical efficiency and quality. Comprises the identification, analysis, and interpretation of trends or patterns. Works closely with business analyst and product manager to advise and jointly brainstorm on how analytics can assist in larger problem solving. Covers the blending of historical data from available customer network data sources, public information, field reports, or purchased sources and the performing of analysis to support business and product decisions.


 

Programming Skills – knowledge of statistical programming languages like R, Python, and database query languages like SQL, Hive, Pig is desirable. Familiarity with Scala, Java, or C++ is an added advantage.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 dataExcellent Communication Skills – it is incredibly important to describe findings to a technical and non-technical audience.Strong Software Engineering BackgroundHands-on experience with data science toolsProblem-solving aptitudeAnalytical mind and great business senseDegree in Computer Science, Engineering or relevant field is preferredProven Experience as Data Analyst or Data Scientist

 

Drive and execute AI/ML projects within Supply Chain. 

Consult and engage with front-end customer teams.

Create, follow up and manage AI/ML roadmap. 

Propose solutions and strategies to tackle business challenges

Developing prediction systems and machine learning algorithms

Presenting results in a clear manner