Data Scientist - APM

• Smiths Detection is seeking an experienced Senior Data Scientist to join the company on a permanent basis. The successful candidate will bring a strong background of using AI/ML/DL algorithms and techniques. You are expected to contribute your proven experience as a Machine Learning Engineer.
• Smiths Detection's Digital Platform is an innovative global security platform designed to transform the way government agencies and organizations secure and manage their ports, airports, infrastructure and border checkpoints. It brings all security operation information together onto a single, integrated and secure platform to achieve 100% data inspection of all cases. Using insights produced through machine learning, artificial intelligence (AI) and data science, our platform enables people to make informed decisions quickly to protect against any border security threat.
• Smiths Detection is looking for dynamic, innovative and motivated software architects to be the part of its global platform and applications development team in Technology group.
• The purpose of this role is to develop, sustain and enhance technical architecture of Smith's cloud (and on-premise) based Platform and its applications.
•  The Senior Data Scientist is part of the overall Platform and Application Architecture group, working closely with Sales, Products & Technology, Program Management, Service and overall business management to deliver successful pursuit outcomes and grow customer engagement, intimacy and our opportunity pipeline.
• This person uses professional concepts and company objectives to resolve complex issues in creative and effective ways. The role brings the capabilities of solution architecture and design using latest tech stack and in house developed platform. He/she applies extensive technical expertise and has full knowledge of other related disciplines. The role requires person to be hands-on software developer.

Responsibilities:

• Study and transform data science prototypes
• Design machine learning systems
• Research and implement appropriate ML algorithms and tools
• Develop machine learning applications according to requirements
• Select appropriate datasets and data representation methods
• Run machine learning tests and experiments
• Perform statistical analysis and fine-tuning using test results
• Train and retrain systems when necessary
• Extend existing ML libraries and frameworks
• Keep abreast of developments in the field
• Collaborate with engineering and product development teams