Senior Data Scientist Job in Qburst

Experience3 - 6 years

Location

• India - Bangalore
• India - Calicut
• India - Chennai
• India - Cochin
• India - Koratty
• India - Trivandrum

Description

We are looking for a Data Scientist who will support our customers and product development initiatives in advanced machine learning projects. The ideal candidate is good at working with numbers and has an interest in engineering mathematics, probability and statistics. The candidate should have strong programming skills and is adept at deep learning and statistical machine learning concepts.

Responsibilities

• Work with stakeholders throughout the organization to understand requirements
• Define architecture for machine learning projects considering the best case scenarios and trade-offs
• Explore research papers to come up with the best models and algorithms that can solve a given problem
• Gather, clean, and annotate data for training machine learning models
• Build tools to facilitate training data generation and accelerate the annotation process
• Build predictive model pipelines based on available research and deploy them
• Customize model architectures to suit various problems
• Define quantitative metrics and assess the effectiveness and accuracy of models to find the best fit one
• Deploy the models in cloud or on-premise infrastructure, and scale the model to achieve the required throughput

Requirements

• Strong programming skills in one or more languages
• Experience in designing and implementing machine learning pipelines in production in at least one industry domain
• Hands on experience in data collection, cleansing, and labelling. Clarity on approaches to minimize manual effort in these, and related best practices
• Good knowledge in Natural Language Processing, Computer Vision, Audio processing and other applications of Deep learning. Handson experience with the state of the art models in any one
• Good understanding of engineering mathematics, probability and statistics
• Ability to read through latest research papers in required domains and understand the concepts behind model architectures enough to modify them
• In-depth knowledge in statistical machine learning. Clear understanding on selection of algorithms suitable for a given problem

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