JOB DESCRIPTIONAnalyze complex datasets using advanced statistical methods and tools, such as Python, Grafana, and others to derive actionable insights, develop smart business solutions and drive business recommendations.Develop and maintain interactive dashboards and visualizations within analytic tools (Grafana) to effectively communicate data insights to business stakeholders.Create and define key performance indicators (KPIs) to measure the effectiveness of data-driven initiatives.Continuously learn and stay updated on industry trends, best practices, and emerging technologies in the data engineering and analytics space.Utilize Python and SQL for data extraction, transformation, and loading (extract, transform, load -ETL) processes.Perform data validation and enrichment to ensure accuracy and consistency.Create and maintain data models and metadata documentation.Partner with stakeholders across business units (ex: sales, finance, security, compliance) to develop analyses and documentation in a collaborative way, communicating effectively and efficiently with production, managerial, and executive teams.Utilization of business sense and communication skills to influence how the organization tackles and resolves business challenges. Additional duties and responsibilities as assigned.Experience and Qualifications:The successful Data Scientist should have core experience and skills in computer science: data structures, algorithms, relational databases, and model design including experience in several coding languages:Considerable experience in open-source data mining, machine-learning, and operations research including regressions and classification models.Good knowledge of Python and SQL (accessing databases).Strong math, analytical and critical thinking skills, with business acumen.Persuasive communication and presentation skills.Working experience with cloud computing services such as AWS, Google Analytics, Redshift or Google Cloud Platform.Familiarity with version control tools, such as Git and GitHub required.When evaluating the candidate, special attention will be paid to the following competencies:Intellectual Curiosity and Critical Thinking, are necessary to analyze a problem from multiple perspectives and examine the interrelationships between data that may appear superficially unrelated.Autonomous, to own projects end-to-end; ability to work on the entire stack without being dependent on other team members.Focus, to design and evaluate a technique over specific time periods, finding it doesnt work, learning from the failure, and trying again.Attention to Detail, in order to maintain rigor and to detect and avoid over-reliance on intuition when examining data.Clear and concise written and oral communication, in order to communicate complex quantitative analysis in a clear and precise manner.People skills are necessary to build good working relationships and work as part of a team.