Data Analyst

Job highlights

• Knowledge and experience in Deep Learning-based architectures (DNN / CNN/ RNN/ LSTM / SSD/LSTM-RNN for Fine-tuning / Transfer Learning / Training),Object Detection,Classification,Segmentation task-based models,etc
• . A drive to learn and master new technologies and techniques.
• Experience using statistical computer languages (Python,SQL,etc.)

Roles and Responsibilities as a Data Analyst.

• Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive solutions.

• Mine and analyse data from company databases to drive optimization and predictive modelling to increase and optimize product efficiency.

• Assess the effectiveness and accuracy of new data sources and data gathering techniques.

• Selecting features, building, and develop custom data models and algorithms to apply to data sets using machine learning techniques. Contribute across advanced stages of deep learning model development including data collection, data cleaning, model development, validation, and deployment.

• Coordinate with different functional teams to implement models and monitor outcomes.

• Draw insights and find solutions by manipulating Satellite imagery, vertical imagery, ocean level imagery, GPS traces, etc. individually and by fusing several combinations of these sources. Use these sources individually or their combinations to predict various map features like route optimisation and attributes by training statistical models/ ML models

Desired Candidate Profile.

• Strong problem-solving skills with an emphasis on data science techniques.

• Experience using statistical computer languages (Python, SQL, etc.) to manipulate data, draw insights and build advanced machine learning and deep learning models from large data sets.

• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, random forest, artificial neural networks, etc.), advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, and proper usage, etc.) and their real-world advantages/drawbacks.

• Hands-on with numpy, pandas, sklearn, OpenCV, Tensor Flow, Caffe, Keras, Pytorch, CUDA, OpenCL, OpenGL

• Knowledge and experience in Deep Learning-based architectures (DNN/CNN/ RNN/ LSTM/SSD/LSTM-RNN for Fine-tuning/Transfer Learning/Training), Object Detection, Classification, Segmentation task-based models, etc. Customization of NN and improving performance

• Excellent written and verbal communication skills for coordinating across teams.

• A drive to learn and master new technologies and techniques.