Data Scientist / Machine Learning Engineer

Job Description:

The right candidate has (Responsibilities);

• Design machine learning systems, and oversee the platform on which the solutions would be deployed. Create ML model training, validation and hyper-parameter search pipelines
• Partner with Analytics and AIML teams to develop and analyze features at scale. Provide SME level interface for team members to optimize their workflows, streamline operationalization and reduce time-to-market
• Contribute to adoption of CI/CD (DevOps), Data Ops and ML Ops practices within Data analytics, AIML and Visualization domains
• Develop distributed applications on-prem as well as on Cloud that scale to serve ML models, analytics, rules, web-applications, and Visualizations for end-users
• Production deployment and Model monitoring to ensure stable performance and adherence to standards
• Develop libraries to ease development, monitoring and control of data and models
• Evaluate state-of-art AIML-centric technologies and prototype solutions to improve our architecture and platform
• Work with business teams to understand requirements and develop relevant solutions. Create presentations / visualizations for the leadership and business that would be able to explain complex outcomes and emphasize business impact.
• Define best practices, guidelines, and guard-rails for AIML Development and Operationalization. Create a strategic roadmap to increase adoption and adherence to standards.
• Lead data science and engineering team in a distributed agile framework

Skill Set:

Primary skills

• Experienced professional with 12+ years of experience working towards design, architecture, development, and operationalization of Artificial Intelligence and Machine Learning models across Big Data Ecosystem (Hadoop, Spark, Kafka, MongoDB) as well as Cloud Platforms (Databricks, MLFlow, GitHub, AKS, and Snowflake)
• Proficiency in applied Machine Learning (End-to-End) Lifecycle and Operationalizing AIML models in Production
• Experience in architecture, design, and implementation of data intensive applications for practical use-cases
• Programming Languages and Frameworks – Expertise in Python, (Py)Torch, (Py)Spark, Advanced SQL, and Shell (Scripting)
• Expertise in Data analytics and Data wrangling through complex and optimized Python / Spark / SQL
• Experience working on cloud platforms – Azure (Databricks, Snowflake), AWS, and their respective offerings
• Experience and understanding across key SQL and NoSQL datastores as well as Stream Data processing (Kafka) and Workflow orchestration technologies
• Flair for Data analytics and Machine Learning and an intuitive understanding on how to bring efficiency in Analytics and ML life cycle