WHO WE ARE
WHAT WE DO
JOIN OUR TEAM
Perform undirected research and frame open-ended industry questions for understanding driver telematics/driving habits real time.
Extract huge volumes of data from multiple internal and external sources like sensors, cell-phones etc.
Employ sophisticated analytics programs, machine learning, deep learning and statistical methods to prepare data for use in predictive and prescriptive modelling for extracted data to understand the behavioural patterns of driving
Thoroughly clean and prune data to discard irrelevant information
Explore and examine data from a variety of angles to determine hidden weaknesses, trends and/or opportunities.
Devise data-driven solutions to the most pressing challenges in understanding driving behaviours and scoring the driving instantaneously
Build new algorithms to solve problems and build new tools to automate the process
Communicate predictions and findings to management and IT departments through effective data visualizations and reports on a regular basis.
Recommend cost-effective changes, improvisations to existing procedures and strategies
Programming: Python (NumPy, Pandas, Scikit-learn, MatplotLib, Seaborn), Pyspark, R
Machine Learning/AI Tools: (Linear Regression, Logistic Regression, Naive Bayes, Decision Trees, Random Forest, Support Vector Machines (SVM), Hierarchical clustering, K-Means Clustering, K-Nearest Neighbours (KNN), Random Forest, Gradient Boosting Trees, Ada Boosting, PCA, T-distributed Stochastic Neighbour Embedding (t-SNE), LDA, Natural Language Processing, Artificial Neural Networks, Convolutional Neural Networks, RNN, Deep Learning on AWS, Cloud AI)
Data Visualization and Reporting: Tableau, Python (MatplotLib, Seaborn), R(ggplot2)
Risk Analysis, Statistics and Math
Data Mining, Cleaning and Munging
Big Data Technologies
Tensor Flow, Keras, AWS ML, GCP, NLTK, MS Office Suite, Google Analytics, GitHub, AWS—(EC2/Lambda/Docker/Kubernetes)
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