Data Scientist

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Job Description:
Day to Day job Duties:
Design and develop predictive models, Client algorithms, statistical analyses and feature engineering to solve complex problems for hardware feedback, ease of use for customers, retrospective data analysis and expanding product capabilities
Evaluate the performance of AI/Client/Computer Vision models/algorithms using relevant metrics, ensuring they meet industry-specific requirements.
Participate in the full lifecycle of AI projects, from ideation and model development to implementation, validation, and deployment.
Create informative and visually appealing data visualizations and reports.
Identify opportunities for innovation and improvement in analytics and data-driven decision-making.
Communicate complex computer vision concepts, and project outcomes to a diverse audience, including technical and non-technical stakeholders.
Provide mentorship and guidance to junior AI scientists and data scientists
Basic Qualifications:
P & C Insurance domain Knowledge is mandatory
Experience in one or more Client toolkits or Python frameworks
Experience and demonstrable knowledge of Deep Learning concepts
Understanding of probability and statistics and machine learning concepts such as precision, recall, optimization, hyperparameter tuning, overfitting, and interpretability
Experience in applying standard implementations of machine learning algorithms effectively by choosing a suitable model such as decision tree, knn, neural net, or an ensemble of multiple models
Understanding of how components and processes work together and communicate with each other using library calls, REST APIs queueing/messaging systems and database queries
Understanding of system design to avoid bottlenecks and let your algorithms scale well with increasing volumes of data
Self-driven and self-guided in a dynamic environment
Experience with PyTorch, NLTK, SciPy, Scikit-Learn, Numpy, OpenCV or equivalent for image preprocessing
Experience with SQL/NoSql databases and queries
Familiarity with coding best practices, OOD/OOP, modular design, SOA, and systems architecture
Technical skills:
Python, pyspark or R programming language for coding
Kubernetes and docker for deployment
AWS sagemaker or EC2 instances for cloud
MYSQL, Oracle, mongodb or Redshift DB for database
Python and pyspark for programming
Cloudera Distributed Platform for computing & deployment
deep learning/neural networks packages like pytorch, tensorflow in python and use GPU
for training distributed computing pyspark and parallel compute in libraries in python
Degree: Bachelors in Computer Science or equivalent work experience