Data Science - Lead Job in Yash Technologies

YASH Technologies, a leading enterprise business and technology solution partner for medium and large global customers, is seeking to hire "Data Scientist" who thrives on challenge and desires to make a real difference in business world. With an environment of extraordinary innovation and unprecedented growth, this is an exciting opportunity for a self-starter who enjoys working in a fast-paced, quality-oriented, and team environment.

What you should have?

• 7+ years of experience in Data science solution development and productionizing.
• An ideal candidate will have a background in software engineering and data science with expertise in machine learning algorithms, statistical analysis tools, and distributed systems.
• Deep understanding of ML algorithms, their fundamentals, and mathematical underpinnings.
• Strong algorithmic thinking, creative problem solving and the ability to take ownership and do independent research.
• Experience scaling machine learning on data and compute grids.
• Proficiency with K8n, Docker, Linux and cloud compute.
• Experience with Dask, Airflow and MLflow.
• MLOps, CI/CD, Git and Agile processes.
• Exposure to Core Java, REST API Frameworks: JAX-RS/Jersey/Spring.
• Experience with Big data, Distributed computing, data mining.
• Experience with Java, Spark, Spark MLLIB, Tensorflow.
• Solid foundation in data structures and complex algorithms.
• Ability to write robust code in Python, Java and R.
• Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikitlearn).
• Experience working with large data sets (Knowledge on getting data from cloud, Hadoop ecosystem).
• Data Visualization - Experience in one or more Data Visualization tools (MS Power BI, Tableau etc.).
• Experience productionizing Machine Learning models, in the cloud (Azure, GCP or AWS preferred).
• Bachelors Degree in physics, Mathematics, Engineering, Metallurgy or ComputerScience.
• MSc in relevant field - Physics, Mathematics, Engineering, Computer Science, Chemistry orMetallurgy.

What you will do?

• Assist in designing data capture / experimental setups for exploratory work, or changes in process / data capture in existing systems.
• Work with the Data Scientist to develop statistical models, algorithms and/or machine learning algorithms to analyse data and address a particular business question.
• Assist and supervise Data Engineers in developing and deploying production workflows for taking data in real-time or periodically from business functions, passing the data through the developed models and producing the relevant reporting without human intervention.
• Support on reporting from analytics tools and developed models.
• Support in architecting data warehouse and data-lake.
• Work with the Data Scientist to design and implement training and deployment approaches for data Science and machine learning model components.
• Develop and deliver a CI/CD pipeline + monitoring for the deployed models.
• Develop a state-of-the-art data science and ML runtime stack in a multi-cloud environment.
• Be hands-on where required and lead from the front in following best practices in development and CI/CD methods.

• Own delivery of features from top to bottom, from concept to code to production.
• Develop tools and libraries that will enable rapid and scalable development in the future.
• Architect key paradigms, pipelines and other mechanisms to take ML systems from proof of concepts to realities in Production.
• Successfully devise and implement strategies to ensure ML heavy systems operate with high accuracy in Production and adapt to discovered needs.
• Lead on software engineering and software design for ML components.
• Understand and use computer science fundamentals, including data structures, algorithms, computability and complexity and computer architecture.
• Manage the infrastructure and pipelines needed to bring models and code into production.
• Research and implement best practices to improve existing machine learning infrastructure.
• Collaborate with data engineers, application programmers and data scientists.