Job Location: Remote
As a data analysist for ADAS you will get the opportunity to work on cutting edge technology projects to understand and evaluate key sensor performance and behaviour that will be used in the development of driver assistance solutions. Part of your responsibility will be to prototype and implement analytics solutions in collaboration with internal function developers and suppliers.
Responsibilities:
Your main responsibilities will be:
- Define sensor KPI targets and develop test methods for KPI measurements.
- Develop software, algorithms and applications to apply mathematics models to set of data collection to understand sensor function and outputs.
- Create, scripts to compare multiple data sets from different sensors.
- Correlate data, evaluate and summarize to team and management.
- Perform exploratory data analysis.
Qualifications:
- MS/BS Degree in Computer Science, Mathematics, Information Technology, Machine Learning, Operation Research, Statistics or equivalent expertise related field of study.
- Expertise in using Python
- Good knowledge of python data analysis libraries (Numpy, Pandas, Scikit etc) and visualization libraries (matplotlib, seaborn, plotly etc).
- Experience in machine learning and deep learning libraries in Python (Keras, Tensorflow etc).
- Experience in Cloud based technologies, Big Data technologies (Hadoop), programming languages ( C/C++, SQL), data science software platforms (Power BI, Qlik View, Spark etc).
- Experience in version control system (GIT, Github etc).
- Ability to work in a small and self-driven team is highly desired. Should be curious and inquisitive, take strong interest in diving deep on analysis.
- Ability to work in international environment travel abroad. Should possess good presentation and communication skills. Demonstrates ownership and organized approach to drive activities / tasks associated.
- C Driver license is a merit.
Mandatory Technical Skills :
- Proven experience in Data modelling Regression, Clustering, Neural Networks, Time series etc.
- Prior experience in processing, cleansing, and verifying the integrity of data used for analysis.
- Should have solved problem by demonstrating data driven predictive analysis and decision-making (inference, automation, real time optimization etc).
- Prior experience in transforming data into useful knowledge; may have extracted business insight from the data and made recommendations.
- Good understanding of machine learning techniques and algorithms, such as kNN, SVM, Decision Forests, k Means etc.
- Hands on experience in building models and data mining using state of the art methods.
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