Data Scientist

Come join our Data team!

High velocity, high intensity, high trust, high bar, high impact, and a will to win.

If those words resonate deeply with you, this could be your next career move. We're seeking someone who leads with humility, pursues audacious goals, and is motivated by meaningful impact on people and the world.

At Future Fit AI, our core mission is to help more people get to better jobs faster and cheaper, with a specific focus on those facing barriers to opportunity. Our work helps resolve the growing issue of economic inequality, ensuring that no one is left behind in the future of work. Our AI-powered platform brings efficiency and insight to workforce development, replacing outdated systems and unlocking human potential at scale.

Ready to make an impact? Apply today.

We're seeking a Data Scientist to join our team. You will build the models at the heart of our product: the systems that connect people to the right jobs, skills, and pathways. This is hands‑on, applied data science on real workforce problems, working with skills and occupation taxonomies, labor market data, and the matching and recommendation systems that turn that data into better outcomes for job seekers.

You will partner closely with Engineering, Product, and our Director of Data & AI to take models from idea to production.

• Applied modelling: Build, evaluate, and ship models for matching, recommendation, and ranking that directly shape the job seeker experience.
• Skills and jobs data: Work with skills, occupation, and career taxonomies and labor market data, improving how we represent and reason about the world of work.
• Production partnership: Collaborate with Engineering to move models into production reliably, and monitor and improve them once they are live.
• Clear analysis: Translate messy, real‑world data into clear findings and recommendations that the team and our customers can act on.

• Strong applied data science experience (roughly 4+ years), with a track record of shipping models that made it into a real product.
• Explicit jobs‑and‑skills or workforce data experience, OR experience with closely related data where there is a clear pathway to apply it to workforce problems (this is a firm criterion for the role).
• Fluency in Python and SQL, and solid grounding in machine learning, NLP, and recommendation/matching techniques.
• Comfort working with large, imperfect datasets and making sound judgment calls about them.
• Clear communication: you can explain a model and its tradeoffs to a non‑technical audience.

• Experience with recommender systems, ranking, or search at scale.
• Familiarity with skills/occupation frameworks (e.g., O
  * NET, ESCO) or HR/labor market data.
• Experience pairing classical ML with LLMs, including where to use each and how to add guardrails.
• Publications, presentations, blog posts, or other public artifacts showcasing your expertise and depth of knowledge in data science.

• Languages: Python, SQL
• Machine learning and NLP: scikit‑learn, modern NLP and embedding tooling, AWS Sage Maker
• Data orchestration and transformation: Airflow, dbt
• Data storage and warehousing: PostgreSQL, Redshift, MongoDB
• Visualization and reporting: Looker

Your alma mater isn't our focus. Your grit, hunger, and drive are. If you learn continuously, tackle challenges head‑on, and know your strengths and gaps intimately, you're our person.

We are open to candidates living anywhere in Canada or the US. For candidates living in Toronto, our office is conveniently located at 325 Front St West (a short walk from Union Station).

Although this role is remote, you may be expected to travel up to once per quarter for offsites and team gatherings.

The base salary range for this role is USD $100,000 to $140,000 for candidates based in New York and CAD $110,000 to $155,000 for candidates based in Toronto, benchmarked to the middle of the market for comparable venture‑backed companies. This range reflects the varying levels of expertise and responsibilities that will be determined through the interview process, based on applied experience and other criteria established by the hiring committee.

Compensation ranges are reviewed regularly and adjusted to reflect market conditions and cost of living in each location.