Responsibilities: Design data-driven solutions for business problems.
Understand automotive domain specifics; consult experts when needed. Learn and work with vehicle IoT systems, especially the Telematics Control Unit. Build expertise in handling time-series data. 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. Choose and 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. Essential: A 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: o Generalized Linear Models (GLM), Boosting Algorithms, Decision Trees, Neural Networks, Support Vector Machines (SVM), and Bayesian Methods. o Econometric analysis. o Deep Learning models including Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRUs). o Unsupervised learning algorithms and image classification using computer vision. Proven track record of successful model deployment. Desirable: 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.