Kungsbacka: Machine Learning Engineer / Data Scientist Egainer

Machine Learning Engineer / Data Scientist

The Machine learning engineer / Data scientists develops and maintains a high-level understanding of AI, machine learning, Big Data and data science principles. The role actively helps product development by identifying AI opportunities and what value adding services that the customers could benefit from using machine learning. Analysis, cross breeding and refinement of new,existing and third-party data. The role develops machine learning code and smart algorithms, perform data science studies and stays up-to-date on relevant issues and trends within the ML competence field.

Self-motivated professional with senior experience in data science, data modelling, data analysis and programming of advanced machine learning algorithms.   Other critical competencies: Creative and innovative, Expert level problem solving and data analytical skills, above-average conceptual and troubleshooting abilities, Ability to collaborate, quality consciousness, Above average software development skills regarding algorithms and databases, customer understanding and ability to talk with customers and managers of all levels in the language of business. Deep knowledge of math, probability, statistics and algorithms.  

• Analysing data to identify patterns and trends

• Identify the data-analytics problems that offer the greatest opportunities to the organization

• Cleaning and validating the data to ensure accuracy, completeness, and uniformity

• Collect large sets of structured and unstructured data from external sources

• Design and apply models and algorithms that provide value for customers

• Communicating findings to stakeholders using visualization and other means

• Consultee the organization regarding data visualization, big data and machine learning

• Develop algorithms using machine learning techniques

• Understand how statistical bias may affect the relevant data sets

• Account for data collection within the energy domain

• Creating algorithms for anomaly detection and performance tracking

• Integrate machine learning applications with existing solutions

• Write robust code in Python, .Net, R and other relevant machine learning languages

• Familiarity with modern / relevant machine learning frameworks and libraries

• Study and transform data science prototypes

• Design machine learning systems

• Research and implement appropriate ML algorithms and tools

• Develop machine learning applications according to requirements and the current architectural directives

• Select appropriate datasets and data representation methods

• Run machine learning tests and experiments

• Perform statistical analysis and fine-tuning using test results

• Train and retrain systems when necessary

• Extend existing ML libraries and frameworks

• Serve as a subject expert and share knowledge among team members

• Compile timely, comprehensive and accurate documentation and/or reports as requested

• Possess up-to-date knowledge of technological developments in the industry

• Document and communicate solutions by developing documentation, flowcharts, layouts, diagrams, charts, code comments and clear code

• Other responsibilities within the functional area or other areas of the Company as needed