Applied Scientist – Pro Growth

This is a fully remote position, open to applicants in Canada.

📋 Description

• Lead and propel applied science initiatives to fruition, emphasizing the business outcomes of these projects on professional acquisition, activation, retention, and supply vitality.

• Create, implement, and enhance machine learning models aimed at supply-side challenges, which include: lead scoring and professional acquisition targeting, market-level supply forecasting, onboarding and activation of professionals, churn prediction and retention, as well as agentic onboarding processes.

• Design and perform robust marketplace experiments to assess the impact of models and products on supply and business metrics.

• Examine both structured and unstructured data, such as marketplace interactions, professional profiles, and external supply indicators, to uncover trends, opportunities, and risks concerning supply health.

• Work in close collaboration with engineering, product management, business development, and marketing teams to identify challenges, craft comprehensive solutions, and ensure the effective implementation and monitoring of machine learning systems in production.

• Balance the need for rapid execution with scientific rigor while crafting solutions for a dynamic marketplace environment.

• Contribute to the advancement of the team’s agentic-first operational methodologies, including LLM-powered tools, evaluation coverage, and the integration of AI agents into daily applied science activities.

⛳️ Requirements

• Master’s degree in a quantitative discipline (Computer Science, Machine Learning, Statistics, Operations Research, Economics, or a related field), or equivalent experience in the industry.

• Over 3 years of professional experience as an applied scientist, data scientist, or ML engineer with responsibility for production machine learning models.

• Experience in applied science within a marketplace, supply chain, or growth sector.

• Strong understanding of machine learning methodologies such as classification, regression, embedding techniques, and causal inference.

• Familiarity with contemporary large language models (OpenAI, Anthropic Claude, Gemini, AWS Bedrock) and experience with agentic AI development practices and tools.

• Solid foundation in probability, statistics, and econometric techniques, including experimental design, causal inference, and optimization.

• Experience with large-scale distributed systems (Spark, Databricks, or similar platforms).

• Proficiency in reading, writing, and debugging code in programming languages such as Python and SQL.

• Ability to deconstruct complex problems methodically and comprehend the trade-offs necessary for delivering impactful projects.

• Strong communication skills to convey information clearly and effectively to cross-functional partners with varying levels of technical expertise.

• Proven track record of executing end-to-end on at least one production machine learning project seamlessly.

🏝️ Benefits

• Health insurance

• Retirement plans

• Professional development opportunities