Data Scientist

About the role

FutureFit AI is hiring a Data Scientist to join its data team and help power the core product models that connect people with the right jobs, skills, and career paths. This position is deeply hands-on and centered on practical data science challenges in the workforce domain, using skills and occupation taxonomies, labor market datasets, and matching/recommendation systems to improve outcomes for job seekers.

The company works with a fast-moving, high-trust, high-impact mindset and values people who are humble, ambitious, and driven by meaningful social impact. Its mission is to help more people reach better jobs more quickly and at lower cost, especially those facing barriers to opportunity.

What you will do

• Develop, evaluate, and launch applied models for matching, recommendation, and ranking that influence the job seeker experience.
• Work with skills, occupation, and career taxonomies along with labor market data to strengthen how the product represents the world of work.
• Partner with Engineering to deploy models reliably into production and continue monitoring and improving them after launch.
• Turn complex, messy real-world data into clear insights and actionable recommendations for both internal teams and customers.

Required experience and skills

• About 4+ years of applied data science experience, with evidence of shipping models into production products.
• Direct experience with jobs-and-skills or workforce datasets, or closely related data that can clearly be applied to workforce problems.
• Strong command of Python and SQL, plus a solid foundation in machine learning, NLP, and recommendation or matching methods.
• Ability to work confidently with large, imperfect datasets and make sound judgment calls.
• Strong communication skills with the ability to explain models and trade-offs to non-technical stakeholders.

Nice to have

• Experience building recommender systems, ranking systems, or search solutions at scale.
• Exposure to skills or occupation frameworks such as O*NET or ESCO, or to HR and labor market datasets.
• Experience combining classical machine learning with LLMs, including deciding where each is appropriate and how to apply safeguards.
• Public-facing work such as publications, presentations, blog posts, or similar outputs that demonstrate depth in data science.

Technology environment

The data stack includes Python and SQL, scikit-learn, modern NLP and embedding tools, AWS SageMaker, Airflow, dbt, PostgreSQL, Redshift, MongoDB, and Looker.

Location and travel

This role is remote and open to candidates based anywhere in Canada or the US. If you are based in Toronto, the office is at 325 Front St West, a short walk from Union Station. The role may require travel up to once per quarter for offsites and team gatherings.

Compensation

The base salary is benchmarked to the middle of the market for comparable venture-backed companies. For candidates in New York, the range is USD 100,000 to 140,000. For candidates in Toronto, the range is CAD 110,000 to 155,000. Final compensation level depends on experience, responsibilities, and interview outcomes, and the company reviews ranges regularly against market and cost-of-living changes.

Hiring process

The selection process is intended to help both sides assess fit quickly and thoughtfully. It typically includes an online application, an initial screen with the Director of People & Culture, an interview with the hiring manager, a performance challenge, final 1:1 interviews, and a final decision. The full process usually takes about 6 weeks, though timing can vary depending on the candidate.

Education

The company does not place emphasis on where you studied. What matters more is curiosity, persistence, and the ability to keep learning while understanding your own strengths and gaps.

Additional information

FutureFit AI focuses on helping people get to better jobs faster and cheaper, especially in the context of economic inequality and access to opportunity. The company serves workforce development agencies, intermediaries, government agencies, and employers, and operates as a SaaS/AI technology business. The team size is around 30 to 50 people across the US and Canada, with hubs in New York City and Toronto. The company was bootstrapped in its early stage and later raised funding led by JP Morgan.

Core principles

• Be Curious
• Drive to Outcomes
• Raise the Bar
• Speed Matters
• Own It
• We Over Me

Use of AI in hiring

The company uses AI tools to improve the efficiency, consistency, and fairness of hiring, while keeping humans responsible for all final decisions. AI may support application screening against role-specific skills and experience, assist with interview note-taking through an AI notetaker, and provide data points for evaluation. It does not make or recommend final hiring decisions.

Accessibility and equal opportunity

Reasonable accommodation is available for applicants and employees with disabilities during the application process, interviews, and performance of essential job functions, as well as for other employment benefits and privileges. The company is an equal opportunity employer and welcomes applicants from diverse backgrounds, experiences, abilities, and perspectives. Employment decisions are not made on the basis of race, religion, color, gender identity, sexual orientation, age, disability, veteran status, or other protected characteristics.