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Fin

Staff Machine Learning Scientist

London
Posted about 2 months ago
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Staff Machine Learning Scientist

Fin is the AI Customer Agent company 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 for one single system that is designed to meet the needs of modern day support teams.

Founded in 2011, Fin became one of the fastest growing companies and remains one of the largest private software companies in the world with nearly 30,000 global businesses using our products to transform their customer support. Driven by our core values, we push boundaries, build with speed and intensity, and relentlessly deliver incredible value to our customers.

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

Reasons to use Rodeo

I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?

Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.

Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.

Start with a chat, not a search bar

Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.

P

Graduate Consultant — 2026 Scheme

PwC·London, UK
£35,000/yr

Why you're a good match

Strong

Your economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.

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It searches the market for you

Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.

Why you're a good match

You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.

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Strong

Experience fit

Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.

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Strong

Only hits

No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.

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.

Benefits

We are a well treated bunch, with awesome benefits! If there’s something important to you that’s not on this list, talk to us!

Competitive salary and equity in a fast-growing start-up We serve lunch every weekday, plus a variety of snack foods and a fully stocked kitchen Regular compensation reviews - we reward great work! Peace of mind with life assurance, as well as comprehensive health and dental insurance for you and your dependents Open vacation policy and flexible holidays so you can take time off when you need it Paid maternity leave, as well as 6 weeks paternity leave for fathers, to let you spend valuable time with your loved ones If you’re cycling, we’ve got you covered on the Cycle-to-Work Scheme. With secure bike storage too MacBooks are our standard, but we’re happy to get you whatever equipment helps you get your job done Unlimited access to Claude Code and best-in-class AI tools; experimentation & building is encouraged & celebrated

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We are a diverse bunch of people and we want to continue to attract and retain a diverse range of people into our organisation. We're committed to an inclusive and diverse Fin! We do not discriminate based on gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race.

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.

Fin values diversity and is committed to a policy of Equal Employment Opportunity. Fin will not discriminate against an applicant or employee on the basis of race, color, religion, creed, national origin, ancestry, sex, gender, age, physical or mental disability, veteran or military status, genetic information, sexual orientation, gender identity, gender expression, marital status, or any other legally recognized protected basis under federal, state, or local law.

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Skills

Machine Learning
Data Analysis
Programming
Communication
Algorithm Research
Statistical Analysis
NLP
Deep Learning
Reinforcement Learning
Clustering
SQL
Visualization
Team Leadership
Product Development
Operational Excellence
Exploratory Data Analysis

Location

London, England, United Kingdom

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