About Company:
- LatentView Analytics is a leading global analytics and decision sciences provider, delivering solutions that help companies drive digital transformation and use data to gain a competitive advantage. With analytics solutions that provide a 360-degree view of the digital consumer, fuel machine learning capabilities, and support artificial intelligence.
- We specialize in Predictive Modeling, Marketing Analytics, Big Data Analytics, Advanced Analytics, Web Analytics, Data Science, Data Engineering, Artificial Intelligence and Machine Learning Applications.
- LatentView Analytics is a trusted partner to enterprises worldwide, including more than two dozen Fortune 500 companies in the retail, CPG, financial, technology and healthcare sectors.
Job Description:
- Combine statistics, NLP, and machine lear:ning techniques to create scalable solutions for business problems.
- Extensive ML and Deep Learning (NLP focus) experience.
- Hands-on experience with text preprocessing, named entity recognition and entity linking, topic modeling, document classification, and summarization.
- Expertise in any 4 to 7 Data Science Models – Hypothesis Testing, Linear Regression, Logistic Regression, Clustering, ANOVA, Principal Component Analysis, Conjoint -Analysis, Neural Networks, Decision Trees, Ensemble Methods -Focus on scalability, performance, service robustness, and cost trade-offs
- Experience in IT Operations involving AWS/Snowflake/Azure cloud Infrastructure, Machine Learning and Predictive Analytics.
- Knowledge on ML model deployment and experience in ML model life cycle management.
- Knowledge of Sagemaker/Azure ML and Client facing experience is an added advantage.
- Excellent experience in understanding the problem statement, architecture and designing the solution. Experience in building large scale data processing and ML pipelines.
- Hands-on experience in deploying machine learning models in cloud Infrastructure.
- Passion for the engineering process required to train ML/AI models at scale in the cloud.
- Good understanding of ML Ops process, preferably on cloud/experience in applying machine learning techniques and algorithms.
- Experience in Containerizing the ML models and model serving process.
- Good understanding and experience of Model monitoring frameworks
- Experience with containers and microservice architectures e.g., Kubernetes, Docker and server less approaches.
- Knowledge of Deep Learning frameworks: Keras, Tensor flow, PyTorch
