Staff Machine Learning Scientist

Intercom · London, England · 3 weeks ago

Work mode: Hybrid
Seniority: Staff
Category: ML ai
Experience: 5+ years
Visa sponsorship: No
Company size: 501-1000

agentic-ai ai crm data-science deep-learning ecommerce help-desk machine-learning

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.

Unleash Your Potential

When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best, and our AI agents accelerate your impact so you can do your best. Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world.

In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. At Fin, we want to give people what they need to do the best work of their careers. Our benefits and programs are designed to support your health and wellbeing, your family, your time away from work, and your financial future. Offerings vary by location in line with local practices and requirements. Learn more about working at Fin and the benefits we offer at. Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.

Policies

Fin has a hybrid working policy. We believe that working in person helps us stay connected, collaborate easier and create a great culture while still providing flexibility to work from home. We expect employees to be in the office at least three days per week.

We have a radically open and accepting culture at Fin. We avoid spending time on divisive subjects to foster a safe and cohesive work environment for everyone. As an organization, our policy is to not advocate on behalf of the company or our employees on any social or political topics out of our internal or external communications. We respect personal opinion and expression on these topics on personal social platforms on personal time, and do not challenge or confront anyone for their views on non-work related topics. Our goal is to focus on doing incredible work to achieve our goals and unite the company through our core values.

Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.

Staff Machine Learning Scientist

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