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:
4 years
of industry experience in data science.
Python
programming with experience in
pandas
,
NumPy
,
matplotlib
, and
sklearn
libraries.
statistical analysis
, including descriptive, inferential statistics, and hypothesis testing.
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.
