Company Description
Do you want beneficial technologies being shaped by your ideas? Whether in the areas of mobility solutions, consumer goods, industrial technology or energy and building technology – with us, you will have the chance to improve quality of life all across the globe. Welcome to Bosch.
We’re building innovative solutions for the connected vehicle, using the latest technologies to build the connected car of tomorrow. Our portfolio includes smartphone integration, cloud services, and embedded technology. We’re driven by creativity and a desire to shape the future. Our spirit of innovation, combined with Bosch‘s expertise in automotive technology, gives us a unique edge in the market.
Bosch Predictive Road Condition Services
Interested? Get ready to work #LikeABosch!
Job Description
► You will be analyzing potential new data sources regarding their benefit for either our Road Hazard Services or our Ground Truth Database
► You will be ensuring the coverage and quality of our ground truth data (e.g. with manual video verifications or automated anomaly detections). Set up data cleaning pipelines in collaboration with our data engineers
► You will be generating new ground truth data by e.g. implementation of automated video labeling
► You will be defining quality metrics for our Road Hazard Services
► You will be seting up KPI dashboards and automating KPI reports in collaboration with our data engineers
Qualifications
► You have 3+ years of experience in statistical data analysis (will be considered as a plus experience in analysis of spatio-temporal data (e.g. vehicle sensor data))
► You have experience in definition and evaluation of data quality metrics and setting up of KPI dashboards or reports
► In order to succeed in this role, having a strong understanding of SQL is essential.
►Understanding of data models( will be considered as a plus knowledge in Azure Data Explorer (ADX) and Kusto Query Language (KQL) )
► Python and relevant data science libraries are required (Pandas, Scikit-Learn, Matplotlib etc.)
► You are dedicated to developing clean and maintainable analysis code and ensuring the correctness of the evaluation results
► You have deep understanding of the product goals and performing data analysis in a pragmatic and target-oriented manner
► You have proactive approach to identify and address potential issues (e.g. data anomalies), contributing to the continuous improvement of processes and implementations.
► You have strong analytical capabilities for identifying potential data issues in a very structured working style
► You are strong team player with a collaborative mindset, excelling in cross-functional and interdisciplinary teams (e.g. collaboration with data engineers)
► You have excellent communication skills, capable of articulating insights of data analysis and KPIs to both technical and non-technical stakeholders.
Additional Information
#LikeABosch Benefits:
Your work-life balance is valuable to us, so we offer you:
- 22 - 27 days of annual leave, because work-life balance is essential to us
- Flexible working hours
- If you want to work from somewhere else in Serbia, feel free, our Hybrid program helps you do that
- Up to 3 days off for private purposes
Your health and well-being is important to us, so we have you covered:
- Medical subscription for annual health check ups
- Mental health and well-being initiatives
- Private health insurance
We believe in a Life-Long Learning approach, so you can take advantage of:
- Technical and soft skills trainings
- English and German language courses
- Access to e-learning platforms
- Local and Global career development programs
- Opportunity to attend conferences
On top of these, we are offering:
- Remote working environment (in borders of Serbia)
- Canteen (when working in office) with lunch discounts
- Annual bonus
- Referral bonus
Ready to work #LikeABosch?
We live by our values, have an open feedback culture and there is plenty of room for innovative ideas. All because a team is only as valuable as its members.
Included in the referral program.
