Client Description:

Our client is a SaaS technology. This platform has already added 30+ global clients to date, across multiple domains like Engineering and Construction, Energy & Utilities, Government, Legal, and SCM & Logistics. The company has ramped up to a strength of 40+ and has also achieved prestigious industry recognitions.

Role Details:

• Title / Designation: ML Scientist
• Location: Hyderabad, India

Desired Skills Set:

• Proven experience as a Machine Learning Scientist/Data Scientist or similar role
• Should have experience in leading a team of ML engineers, designing the solutions based on AI/ML, proficiency in programming languages like Python, R, and JavaScript, grasp on different ML libraries / Services / APIs like Microsoft Cognitive Services /AWS AI and ML services/Google Cognitive Services.
• Ability to choose an appropriate tool based on requirement and to compose different ML components and create new service/product.
• Understanding of data structures, data modeling, and software architecture
• Strong knowledge of Machine Learning frameworks such as TensorFlow & Keras or     PyTorch and libraries like Scikit-learn.
• Word embedding such as Word2Vec & Sentence2Vec, Wav2Vec 
• Experience with Machine Learning techniques for supervised & unsupervised learning, e.g., CNN's, DNN's & RNN's 
• Knowledge of relational databases (e. g. MS SQL Server, MySQL) and NoSQL databases (e. g. MongoDB), C#, ASP.Net, Angular Framework, and REST APIs & JSON 
• Experience working on Agile Methodologies. 
• Strong in fundamentals of Natural Language Processing, Machine Learning algorithms, models, and principles. 

Role Responsibilities:

• Lead and mentor machine learning team
• Research, design, and frame machine learning systems
• Understand business objectives and apply appropriate ML techniques to solve business problems.
• Select appropriate datasets and data representation methods
• Perform machine learning model tests and experiments
• Perform statistical analysis and fine-tuning using test results
• Extend existing ML libraries and frameworks
• Verifying data quality, and/or ensuring it via data cleaning
• Collaborate with different stakeholders and product teams in implementing/using AI/ML products.
• Manage the infrastructure and data pipelines needed to bring code to production
• Select and implement the right machine learning algorithms
• Select the right training Data Sets for ML model development

Assessment & interview process:

• 2 rounds of technical interviews
• 1 Managerial Round.