Staff Machine Learning Scientist

What's the opportunity?

Fin's Machine Learning team is responsible for defining new ML features, researching appropriate algorithms and technologies, and rapidly getting first prototypes in our customers’ hands.

We are an extremely product focussed team. We work in partnership with Product and Design functions of teams we support. Our team's dedicated ML product engineers enable us to move to production fast, often shipping to beta in weeks after a successful offline test.

We are very passionate about applying machine learning technology, and have productized everything from classic supervised models, to cutting-edge unsupervised clustering algorithms, to novel applications of transformer neural networks. We test and measure the real customer impact of each model we deploy.

What will I be doing?

• Play an active role in hiring, mentoring and career development of other engineers
• Raise the bar for technical standards, performance, reliability, and operational excellence
• Identify areas where ML can create value for our customers
• Identify the right ML framing of product problems - Working with teammates and Product and Design stakeholders
• Conduct exploratory data analysis and research - Deeply understand the problem area
• Research and identify the right algorithms and tools - Being pragmatic, but innovating right to the cutting-edge when needed
• Perform offline evaluation to gather evidence an algorithm will work
• Work with engineers to bring prototypes to production
• Plan, measure & socialize learnings to inform iteration
• Partner deeply with the rest of team, and others, to build excellent ML products

What skills might I need?

• 5-8 years applied ML experience
• Previous background in a senior/staff role (data science, software development or academic)
• Significant, demonstrated impact that your work has had on the product and/or the teams
• Strong programming skills
• Experience as the primary technical leader for a team
• Strong communication skills, both within engineering teams and across disciplines.
• Comfort with ambiguity
• Typically have advanced education in ML or related field (e.g. MSc)
• Scientific thinking skills

Bonus skills & attributes

• Track record shipping ML products
• PhD or other experience in a research environment
• Deep experience in an applicable ML area - E.g. NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering
• Strong stats or math background
• Visualization, data skills, SQL, matplotlib, etc.