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Quality Data Analyst
hace 3 semanas
About Us
HORSE is a global leader in the automotive industry, dedicated to pioneering low-emission and alternative-fuel power solutions. With a presence across seven countries, our mission is to drive the transition to a sustainable future by developing next-generation hybrid, synthetic, and hydrogen-fueled powertrains. We believe in a pragmatic approach to decarbonization, recognizing that there is no one-size-fits-all solution to sustainable mobility.
At the heart of our success is our people. We foster a culture of innovation, collaboration, and inclusivity, ensuring that every team member feels valued and empowered. Our global footprint not only drives technological advancement but also generates opportunities for personal and professional growth.
Join us at HORSE, and be part of a team that’s transforming the future of mobility, while building a career that matters.
What you will do- Using automated tools to extract data from primary and secondary sources, removing corrupted data and fixing coding errors and related problems.
- Developing and maintaining databases and data systems – reorganizing data in a readable format.
- Performing analysis to assess the quality and meaning of data.
- Filtering data by reviewing reports and performance indicators to identify and correct code problems.
- Using statistical tools to identify, analyze, and interpret patterns and trends in complex data sets to aid diagnosis and prediction.
- Assigning numerical value to essential business functions so that business performance can be assessed and compared over periods of time.
- Analyzing local and global trends that impact both the organization and the industry.
- Preparing reports for management stating trends, patterns, and predictions using relevant data.
- Working with programmers, engineers, and management heads to identify process improvement opportunities, propose system modifications, and devise data governance strategies.
- Preparing final analysis reports for stakeholders to understand the data-analysis steps, enabling them to make important decisions based on various facts and trends.
- High level experience in the software project lifecycle is required, including requirements gathering, vendor selection, implementation, testing, and deployment.
- Data Cleaning and Preparation: This includes removing errors, identifying outliers, and transforming data into a format that can be analyzed.
- Data Analysis and Exploration: This includes using statistical methods to test hypotheses, identify trends, and make predictions.
- Statistical Analysis: Familiarity with mean, median, variance, standard deviation, correlation, regression, and hypothesis testing is fundamental.
- Proficiency with Python: Libraries like pandas, numpy, and scikit-learn are essential for data manipulation and analysis.
- Database Management: Ability to query databases using SQL is essential for extracting data.
- Knowledge of database systems like SQL, NoSQL, BigQuery, Looker, Spotfire, Web Analytics, Python, Go, Scala, Java, Apache Beam.
- Creating Dashboards and Reports: Proficiency in tools like Tableau, Power BI, and Microsoft Word to create interactive dashboards and reports.
- Data Visualization: Proficiency in tools and libraries like Tableau, Power BI, Matplotlib, Seaborn, or ggplot2 to represent data in a visual format that's easy to understand.
- Machine Learning Knowledge.
- A supportive and collaborative work environment.
- Opportunities to work on real projects and make a tangible impact.
- Mentorship and guidance from experienced professionals.
- A chance to develop a diverse skill set in communication.
- Benefits: Competitive salary with fixed and variable components, life insurance, and pension plan.
