Senior Data Scientist (Video Analytics) @ Hitachi

Duties and Responsibilities:

• Research and Develop Innovative Use Cases, Solutions and Quantitative Models in Video and Image Recognition and Signal Processing for Hitachi s cross-industry business (e.g., Energy, Industry, Mobility, Smart Life and Financial Services).

• Design, Implement and Demonstrate Proof-of-Concept and Working Proto-types for Hitachi and its clients.

• Provide RD support to Hitachi business units and group companies to productize research prototypes.

• Explore emerging tools, techniques, and technologies, and work with academia for cutting-edge solutions.

• Collaborate with cross-functional teams and eco-system partners for mutual business benefit.

• Generate eminence via patents, publications, thought leadership, invited speakerships and conspicuous presence in forums of repute.

• Add value to self through continuous learning and knowledge acquisition.

• Give back learnings to colleagues and communities.

• Mentor colleagues for growth and success.

Mandatory Requirements:

Academic Qualification: Bachelor s degree with STEM background (Science, Technology, Engineering and Management) with strong quantitative flavour.

Strong Fundamentals: The candidate is required to build quantitative models from first principles and hence need to have excellent understanding of basics in mathematics and statistics (e.g., differential equations, linear algebra, matrix, combinatorics, probability, Bayesian statistics, eigen vectors, Markov models, Fourier analysis).

Core Expertise: The candidate is expected to specialize in Video and Image Signal Processing. Solid understanding of Video and Image Recognition is a pre-requisite. Excellent understanding of Stochastic Pattern Recognition is required. It is good to have hands-on model implementation skills for any one of the above areas using any widely used technology stack (e.g., OpenCV, Pillow, torchvision, TensorFlow, Caffe, Python/Keras)

Emerging Trends - Artificial Intelligence and Machine Learning: Good Understanding of working principles of neural networks and underlying algorithms (e.g., convolutional-CNN), recurrent-RNN-LSTM-GRU, back-propagation, loss function, gradient descent)

Data Wrangling and Model Lifecycle Maintenance: Experience in data cleaning, ETL, pipeline building and model-maintenance, using commonly used methodology (e.g., Airflow, MLflow)

Communication : Ability to articulate key messages concisely and precisely

Collaboration: Excellent interpersonal and teaming skills