Senior Data Scientist - AI Tooling

Intercom
Full-timeData engineeringDublin, Ireland · Remote within Ireland
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At a glance

Employment type
Full-time
Workplace
Remote

Technologies & skills

Primary technologies

Secondary technologies

Other technical skills

What you'll be doing

Senior Data Scientist role on the Research, Analytics & Data Science (RAD) team at Fin, focused on designing, evaluating, and shipping AI-powered internal tools for GTM use cases. High-ownership, end-to-end work from problem definition to production, with a strong emphasis on measurable business impact.

  • Design, evaluate, and ship AI-powered internal tools for GTM use cases
  • Own the full lifecycle from problem definition and data modeling to building production-ready tools
  • Write Python backends and React frontends
  • Rapidly prototype and iterate with users
  • Instrument tools for adoption and measurable business outcomes
  • Document playbooks and run enablement sessions
  • Help leaders operationalize new tooling across teams

Key requirements

Must-have

  • Proven track record of applied data science with measurable GTM impact
  • LLM/ML application experience (e.g., RAG, prompt and tool design, vector search, evals)
  • Excellent SQL skills
  • Fluency in Python or R
  • Experience with orchestration tools (e.g., dbt, Airflow)
  • Strong communication and empathy
  • Collaborative product mindset

Role signals

Technical focus
data science, AI tooling, end-to-end product development
Hands-on vs management
Hands-on

Full job description

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?

The Research, Analytics & Data Science (RAD) team turns insight into action. We uncover customer, product, and business insights and translate them into tools and decision systems embedded directly into GTM workflows.

AI has unlocked an entirely new generation of internal tools for our GTM teams. We’re evolving from static dashboards to LLM and agent-powered workflows that do the work: auto-researching accounts, summarizing prior interactions, drafting personalized outreach, flagging renewal risk, and assembling decks and docs - enabling Sales and Success to focus on high-value conversations.

The RAD team partners closely with GTM Systems to define problems, solutions, and measure impact - from prototype to production.

This is a high-ownership role for someone who thrives in ambiguity, is energized by solving complex real-world problems, and is motivated by seeing their work translate into tangible business impact.

What will I be doing?

  • Design, evaluate, and ship AI-powered internal tools for GTM use cases - including account research & summaries, next-best-action recommendations, renewal propensity, pipeline risk detection, QBR/autobrief generation, and post-call summarization & follow-ups.

  • Work end-to-end: Own the full lifecycle, from problem definition and data modeling to building production-ready tools, including writing Python backends and React frontends.

  • Prototype fast, ship to learn: Rapidly build with users, then productionize quickly to iterate and deliver impact.

  • Instrument for adoption and outcomes: Define success through real usage and measurable business impact (e.g., improved win rate, conversion, expansion).

  • Evangelize and enable: Document playbooks, run enablement sessions, and help leaders operationalize new tooling across teams.

What skills do I need?

  • Proven track record of applied data science with measurable GTM impact - you’ve shipped models or tools that moved metrics like conversion, cycle time, or retention.

  • LLM/ML application experience - familiarity with RAG, prompt and tool design, vector search, evals and have leveraged AI for development.

  • Excellent SQL skills and fluency in Python or R, with experience applying analytical and statistical methods to business problems.

  • Experience with orchestration tools (e.g., DBT, Airflow) for deploying reliable data workflows.

  • Strong communication and empathy - ability to translate complex data concepts for non-technical stakeholders.

  • Collaborative** product mindset **- comfort working closely with Sales and Success teams to turn ambiguity into clear deliverables.

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

  • MacBooks are our standard, but we’re happy to get you whatever equipment helps you get your job done

#LI-Hybrid

**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.

Interview prep pack

Grounded in this listing. Use it to prepare examples before you apply.

Your interview focus

You will design, build, and ship AI-powered internal tools for GTM teams, working end-to-end from data modeling to production. Expect to be assessed on your applied data science skills, LLM/ML experience, ability to rapidly prototype, and strong communication with non-technical stakeholders.

  • AI tooling for GTM·High
  • End-to-end data science·High
  • LLM/ML application·High
  • Rapid prototyping·High

Only have 30 minutes?

Follow a focused preparation plan based on this job.

Start 30-minute prep

Your 30-minute plan

  1. Review AI Tooling Projects

    0–8 min

    List and reflect on your most relevant projects where you built or shipped AI-powered tools, focusing on GTM or business impact.

  2. Refresh LLM/ML Concepts

    8–15 min

    Skim recent notes or resources on LLMs, RAG, prompt engineering, and vector search to ensure you can discuss them confidently.

  3. Prepare Impact Stories

    15–23 min

    Select 2-3 examples where your work led to measurable improvements in business metrics, and outline the problem, solution, and results.

  4. Practice Communication

    23–30 min

    Rehearse explaining a complex data science concept in simple terms, as you would to a non-technical stakeholder.

Likely questions

, 8 items

Priority reflects how strongly this topic is emphasised in the job listing, not whether it will be asked.

Talking points

, 6 items
  • Designing AI-Powered Internal Tools

    You will be expected to build tools that directly impact GTM teams, so understanding how to translate business needs into AI solutions is critical.

  • Applied Data Science for GTM Impact

    Demonstrating experience in shipping models or tools that move key business metrics is a core requirement for this role.

  • LLM/ML Techniques and Tooling

    The role requires hands-on experience with LLMs, RAG, prompt engineering, and vector search, which are central to the AI solutions you'll build.

  • End-to-End Product Development

    You will own the lifecycle from problem definition to production, so you must be comfortable with both backend (Python) and frontend (React) development.

  • Data Workflow Orchestration

    Deploying reliable data workflows using tools like DBT or Airflow is necessary for productionizing your solutions.

  • Communicating Complex Concepts

    Strong communication is needed to explain technical solutions and data insights to non-technical GTM stakeholders.

What to research

, 3 items
  • End-to-End Tool Development

    You will be expected to own the full lifecycle, from problem definition and data modeling to building production-ready tools with both backend and frontend components.

  • LLM/ML Application in GTM Contexts

    Hands-on experience with LLMs, RAG, and prompt engineering applied to GTM use cases is a core requirement and likely a key focus.

  • Measuring and Driving Business Impact

    Demonstrating how your work has led to measurable improvements in business outcomes, and how you instrument for adoption and success, will be critical.

Questions to ask

, 6 items
  1. How does the RAD team prioritize which GTM problems to tackle with AI tooling?

    Why ask this? Clarifies how your work will be directed and how priorities are set.

  2. What does a typical end-to-end project lifecycle look like for internal tool development here?

    Why ask this? Gives insight into workflow, expectations, and autonomy.

  3. How do you measure the business impact of new tools after deployment?

    Why ask this? Reveals the emphasis on outcomes and data-driven decision making.

  4. What are the main challenges the team faces when integrating LLMs into production workflows?

    Why ask this? Helps you understand technical hurdles and learning opportunities.

  5. How does the team collaborate with Sales and Success during tool development?

    Why ask this? Shows how cross-functional relationships are managed.

  6. What opportunities exist for learning and staying current with new AI/ML technologies?

    Why ask this? Assesses support for professional growth.

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Apply NowApply before: 9 Oct 2026