Data Scientist / Technical Analyst

Role Overview: Design data-driven solutions for business problems in the automotive domain, specifically focusing on Telematics Control Unit and time-series data. Collaborate with domain experts to explore data, find insights, and select meaningful features for modelling. Apply suitable ML/DL algorithms, validate models, and mentor junior data scientists in their development. Key Responsibilities: - Design data-driven solutions for business problems in the automotive domain - Understand automotive domain specifics and consult experts when needed - Work with vehicle IoT systems, especially the Telematics Control Unit - Handle time-series data and perform data cleaning, preparation, and ETL processes - Explore data to find insights, trends, and patterns; collaborate with domain experts to test hypotheses - Create and select meaningful features for modelling - Apply suitable ML/DL algorithms, build training pipelines, and optimize models - Validate models and use ensemble methods when beneficial - Visualize and report findings using graphs and summaries - Mentor junior data scientists and support their development Qualifications: - Minimum of 4 years of industry experience in data science - Proficiency in Python programming with experience in pandas, NumPy, matplotlib, and sklearn libraries - Competence in statistical analysis, including descriptive, inferential statistics, and hypothesis testing - Practical experience in mathematical modelling and a variety of machine learning techniques such as Generalized Linear Models (GLM), Boosting Algorithms, Decision Trees, Neural Networks, Support Vector Machines (SVM), Bayesian Methods, Econometric analysis, Deep Learning models including Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTM), Gated Recurrent Units (GRUs), unsupervised learning algorithms, and image classification using computer vision - Proven track record of successful model deployment - Experience with accident simulation tools such as PC crash or OPENPass - Familiarity with time-series and IoT data analytics - Familiarity with LLMs - Knowledge of automotive systems, vehicle fundamentals, and Controller Area Network (CAN) protocol Role Overview: Design data-driven solutions for business problems in the automotive domain, specifically focusing on Telematics Control Unit and time-series data. Collaborate with domain experts to explore data, find insights, and select meaningful features for modelling. Apply suitable ML/DL algorithms, validate models, and mentor junior data scientists in their development. Key Responsibilities: - Design data-driven solutions for business problems in the automotive domain - Understand automotive domain specifics and consult experts when needed - Work with vehicle IoT systems, especially the Telematics Control Unit - Handle time-series data and perform data cleaning, preparation, and ETL processes - Explore data to find insights, trends, and patterns; collaborate with domain experts to test hypotheses - Create and select meaningful features for modelling - Apply suitable ML/DL algorithms, build training pipelines, and optimize models - Validate models and use ensemble methods when beneficial - Visualize and report findings using graphs and summaries - Mentor junior data scientists and support their development Qualifications: - Minimum of 4 years of industry experience in data science - Proficiency in Python programming with experience in pandas, NumPy, matplotlib, and sklearn libraries - Competence in statistical analysis, including descriptive, inferential statistics, and hypothesis testing - Practical experience in mathematical modelling and a variety of machine learning techniques such as Generalized Linear Models (GLM), Boosting Algorithms, Decision Trees, Neural Networks, Support Vector Machines (SVM), Bayesian Methods, Econometric analysis, Deep Learning models including Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTM), Gated Recurrent Units (GRUs), unsupervised learning algorithms, and image classification using computer vision - Proven track record of successful model deployment - Experience with accident simulation tools such as PC crash or OPENPass - Familiarity with time-series and IoT data analytics - Familiarity with LLMs - Knowledge of automotive systems, vehicle fundamentals, and Controller Area Network (CAN) protocol