Job Description

Strong experience in Data Scientist, Machine Learning, Deep Learning, Python, NLP

Job Description:
Key Responsibilities:
Develop time series predictive models for a range of application areas spanning R&D, Manufacturing, and Supply Chain Management.
Work on all aspects of the analytics solution development from building efficient data pipelines to implementing leading-edge inferential methods
Deploy scalable solutions for large datasets
Develop high-quality code for analytics software solutions, primarily with the Python data-science stack
Stay abreast of new developments in the field of time series modeling and forecasting, with a constant eye on how these innovations can be applied to our problems
Work in collaboration with project management to deliver effective and timely solutions
Participate in presenting new results and research innovations internally and externally
Cultivate and grow ties with academia
Mentor interns and new hires
Knowledge & Skills:

Experience using Python and data science related libraries such as pandas, NumPy, scikit-learn, seaborn, Stats-Models, Dask, PySpark
Good knowledge of a deep learning framework (e.g. TensorFlow, keras, PyTorch)
Good practical knowledge with Sequence Models in Deep Learning, like LSTMs, Transformers, etc. especially in the context of Time Series Forecasting
Experience with SQL and relational databases and knowledge of NoSQL databases (e.g. MongoDB).
Experience working in cloud environment Azure, Spark, Kafka or similar.
Experience developing and deploying predictive models and machine learning algorithms into production environments is desired.
Data engineering experience is a must.
Knowledge of and/or experience in the automotive engineering industry is a plus
Strong time management, organizational, and communication skills
Experience with containerizing applications using Docker