Senior Machine Learning Scientist (For independent contractors)

Salary

Core Qualifications

Preferred Qualifications

Key Responsibilities

• •Translate broad business problems into ML/AI challenges and develop a targeted research plan to identify the best approach within the constraints of the production environment.
• •Develop the strategy for machine intelligence on a product family by designing innovative ML/AI models, algorithms, and approaches that deliver both short-term commercial impact and longer-term differentiated business value and customer experiences.
• •Independently define and build proof-of-concepts to test new ideas and demonstrate their potential value to relevant stakeholders.
• •Drive the end-to-end execution of the ML/AI development process on products, from understanding product requirements, data discovery, model development and evaluation, to implementation of a full production pipeline for both batch- and stream-based deployment.
• •Develop production-grade ML code for models, features, and pipelines, accounting for scalability, latency, realtime requirements, monitoring and retraining.
• •Build readable and reusable code, using the right technologies and coding methodologies applying knowledge of business area tools and product needs.
• •Create outsized impact for your work through carefully designed rapid prototyping coupled with strategic scale-up in production through the work of other teams.
• •Promote platform-based development and reuse by coaching teams in abstracting individual business problems to generalized ML/AI products, rather than point solutions, and in identifying horizontal opportunities across multiple business domains.
• •Maintain a highly cross-disciplinary perspective, solving issues by applying approaches and methods from across a variety of ML/AI disciplines and related fields.
• •Coach others through evidence and clear communication, explaining advanced technical concepts in simpler terms.
• •Continuously evolve their craft by keeping up to date with the latest developments in ML/AI and related technologies and upskilling on these, as needed.
• •Responsible for data management related Data Governance processes as defined in the Data Governance Framework (E.g. monitoring of data quality, management of data lineage, and maintenance of logical data model).