Senior Machine Learning Scientist

intercom; Greater London; Hybride; GBP 70 000 - 110 000; Plein temps

Avantages offerts par ce poste

Hybrid work policy

Career growth opportunities

Résumé du poste

Fin, now part of Salesforce, is transforming customer experiences with AI-driven solutions. The ML team builds features that move from research to production, shipping fast to beta learners and customers alike.

The role focuses on identifying high-value ML problems, framing them for products, and collaborating across Product, Design, and Engineering to deliver robust, scalable ML products in a hybrid work environment in London.

Qualifications

• Broad applied machine learning knowledge.
• 3-5 years applied ML experience.
• Practical statistics knowledge (experimental design, confounding).
• Intermediate programming skills.
• Strong communication skills within engineering and across disciplines.
• Comfort with ambiguity.
• Typically have advanced education in ML or related field (e.g. MSc).
• Scientific thinking skills.

Responsabilités

• Identify areas where ML can create value for our customers.
• Define the ML framing of product problems.
• Collaborate with Product and Design stakeholders.
• Conduct exploratory data analysis and research.
• Deeply understand the problem area.
• Research and identify the right algorithms and tools.
• Be pragmatic, but push to cutting-edge when needed.
• Perform offline evaluation to validate models.
• Work with engineers to productionize prototypes.
• Plan, measure and socialize learnings for iteration.
• Partner with the team to build excellent ML products.

Connaissances

Applied ML

Experience 3-5 years

Statistics

Programming

Communication

Ambiguity tolerance

Advanced degree in ML

Scientific thinking

Formation

MSc or higher in ML

Outils

Python

SQL

Matplotlib

Description du poste

Fin, now part of Salesforce, is on a mission to help businesses provide perfect customer experiences.

Our AI Agent Fin is the highest-performing AI Customer Agent on the market today, enabling businesses to deliver impeccable, always-on customer support across the customer journey, from service, to sales, to ecommerce. Powered by our own AI models, Fin resolves complex customer issues end-to-end across every channel, with minimal set-up and integration. Fin can also be combined with our natively integrated Intercom help desk, giving modern support teams one single system.

Together with Salesforce, the #1 AI CRM, where humans with agents drive customer success, we're building the future of customer experience. Here, ambition meets action. Tech meets trust. And innovation isn't a buzzword, it's a way of life. The world of work as we know it is changing, and we're looking for Trailblazers who are passionate about bettering business and the world through AI.

Ready to level up your career at the company leading workforce transformation in the agentic era? You're in the right place. Agentforce is the future of AI, and you are the future of Salesforce.

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?

• 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?

• Broad applied machine learning knowledge
• 3-5 years applied ML experience
• Practical stats knowledge (experiment design, dealing with confounding etc)
• Intermediate programming skills
• 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

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 fin.ai/careers. 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.