Our company is developing intelligent monitoring and critical alarm systems. Kalder is an integration of technology and human expertise that monitors, analyzes and acts to improve performance of supermarket mechanical systems to reduce operating costs.
Some current measuring and control systems can deliver a lot of data at a reasonable cost, but the ways that this measured data is transformed into useful information for maintenance providers and plant managers can be substandard, or even non-existent.
We are looking for a Data Scientist Co-op that will help us discover this hidden information and drive the consumption and display of our IoT data, to help us and our customers make sense of it all. Your primary focus will be in applying data-mining techniques, statistical analysis, and building high quality predictive systems — using machine learning that integrates with our core software product line.
- Work closely with Development, Engineering, Mechanical, and Energy teams to understand the data domain.
- Analyze scoring using machine learning techniques.
- Build analytical, predictive, and recommendation systems.
- Present information and communicate findings using data visualization techniques.
- Preprocess, cleanse, and verify the integrity of data in order to build models and cubes.
- Drive the collection of new data and refine existing data streams.
- Data mine using state-of-the-art methodology.
- Develop prototypes and proof of concepts for predictive models and data analytics.
- Extend company’s data with third party sources of information when needed.
- Perform and interpret data studies and conduct experiments with new data sources or new uses for existing data sources.
- Enhance data collection procedures for robustness and reliability.
- Perform ad-hoc analysis and present results in a clear and concise manner.
- Provide ongoing tracking and monitoring of performance of statistical models.
- Recommend improvements to methods and algorithms leading to new findings.
- Tailor data collection for relevant information to assist in the building of analytical systems.
- Demonstrated experience solving loosely defined problems by leveraging data pattern detection.
- Experience with data-visualization and business intelligence tools.
- Experience working with tools for large data sets: Power BI, Azure ML, SRSS, Spark, Hadoop and Hive.
- Excellent communication skills.
- Familiarity with SQL and NoSQL databases (SQL Server, MySQL, MongoDB and DocumentDB).
- Good applied math and statistical skills: distributions, statistical testing and regression.
- Possess knowledge of analysis tools such as: R and Matlab.
- Passion for empirical research and answering hard questions with data.
- Understanding of machine learning techniques and algorithms.