Data Scientist to support the development and enhancement of a newly established Smart Center in the Distribution Power Division which aims to monitor, automate, and analyze all smart devices within the Distribution Infrastructure, including smart electricity meters, electric vehicle charging stations, and distributed solar. The Data Scientist will support fetching information from various sources and analyze it to understand the Client's power distribution infrastructure performance through the Smart Center. He/she needs to understand and use statistical and analytical methods plus AI tools to automate specific processes within the organization and develop smart solutions to business challenges. The scientist should be able to interpret data, analyze big data, and present the results clearly through dashboards and automated reports. The scientist should analyze trends to support organizational management in making better decisions.
Roles and Responsibilities:
Data mining or extracting usable data from valuable data sources
Using machine learning tools to select features, create and optimize classifiers
Carrying out preprocessing of structured and unstructured data
Enhancing data collection procedures to include all relevant information for developing analytic systems
Processing, cleansing, and validating the integrity of data to be used for analysis
Analyzing large amounts of information to find patterns and solutions
Developing prediction systems and machine learning algorithms
Presenting results in a clear manner
Proposing solutions and strategies to tackle business challenges identified for the Smart Center for Distribution Power
Collaborating with Business and IT teams
Desired Candidate Profile:
Programming Skills: Knowledge of statistical programming languages like R, Python, and database query languages like SQL, Hive, Pig. Familiarity with Scala, Java, or C++.
Statistics: Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators, etc. Proficiency in statistics is essential.
Machine Learning: Good knowledge of machine learning methods like k-Nearest Neighbors, Naive Bayes, SVM, Decision Forests.
Strong Math Skills: Understanding the fundamentals of Multivariable Calculus and Linear Algebra for predictive performance or algorithm optimization techniques.
Data Wrangling: Proficiency in handling imperfections in data.
Experience with Data Visualization Tools: Using TIBCO Spotfire (essential), and other tools such as matplotlib, ggplot, d3.js, Tableau.
Strong Software Engineering Background.
Hands-on experience with data science tools.
Problem-solving aptitude.
Analytical mind and great business sense.
Degree in Computer Science, Engineering, or relevant field is preferred.
Proven Experience as Data Analyst or Data Scientist (Min 10 years of experience).