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About This Role
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 FutureFit 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 Our AI\-powered platform brings efficiency and insight to workforce development, replacing outdated systems and unlocking human potential at scale.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.
*Important note: Data shows that men typically apply when meeting 3/10 requirements, while women often wait until it's 10/10\. We encourage you to apply if you see a strong (not necessarily perfect) fit.*
Your Role
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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.
What You'll Own
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- 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.
Required Experience
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- 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
Bonus Points
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- 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
Our Tech Stack for Data
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- Languages: Python, SQL
- Machine learning and NLP: scikit\-learn, modern NLP and embedding tooling, AWS SageMaker
- Data orchestration and transformation: Airflow, dbt
- Data storage and warehousing: PostgreSQL, Redshift, MongoDB
- Visualization and reporting: Looker
Your Education
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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.
Location
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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).
Travel Expectations
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Although this role is remote, you may be expected to travel up to once per quarter for offsites and team gatherings.
Compensation
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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.
Hiring Journey
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At FutureFit AI, our hiring process is designed to help you assess whether this role and our culture are the right fit based on your unique skills, mindset, and experiences. We move fast and work with intensity, so we want you to get a real sense of that from the start.
Each journey includes a mix of interviews and a performance challenge. For this role, that might look like:
- Online Application
- Initial Screen with Director of People \& Culture
- Interview with Hiring Manager
- Performance Challenge
- Final 1:1 Interviews
- Final Decision
*Generally, this entire process takes around 6 weeks, although the timing can vary due to specific candidate circumstances.*
Ready to shape the future of work?
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At FutureFit AI, we're not just building a company—we're transforming how talent and opportunity connect. Join our driven team united by a commitment to job seekers and the workforce ecosystems we serve.
Company Snapshot:
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- Team: 30\-50 across US and Canada (hubs in NYC and Toronto)
- Customers: Workforce development agencies and intermediaries, government agencies, employers
- Industry: SaaS/AI technology
- Funding: Bootstrapped 0\-1, then raised funding led by JP Morgan
- Structure: Growth, Customer Success, Product, Engineering, Data, People \& Culture, Finance \& Operations
Our Core Principles
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- Be Curious
- Drive to Outcomes
- Raise the Bar
- Speed Matters
- Own It
- We Over Me
Use of AI in Hiring
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At FutureFit, we use artificial intelligence (AI) tools to make our hiring process more efficient, consistent, and equitable—never to replace human judgment. We use AI in the following ways:
- Screening support: AI may help us compare applications against the skills and experience required for a specific role. These skills are defined by the hiring team for each position. A human reviews each application, with the AI assessment as just one input.
- Interview support: In some interviews, we may use an AI notetaker to summarize the discussion so interviewers can focus on being present in the conversation.
- Insights, not decisions: AI provides data points to support our team’s evaluation but does not make or recommend final hiring decisions. Every hiring decision is made by people.
*We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, perform essential job functions, and receive other benefits and privileges of employment. Please contact us to request an accommodation.*
*FutureFit AI All rights reserved, we are proud to be an equal opportunity workplace. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, religion, color, gender identity, sexual orientation, age, disability, veteran status, or other applicable legally protected characteristics. We encourage people of different backgrounds, experiences, abilities, and perspectives to apply.*
Salary Context
This $100K-$155K range is below the median for Data Scientist roles in our dataset (median: $157K across 236 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At FutureFit AI, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($127K) sits 36% below the category median. Disclosed range: $100K to $155K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
FutureFit AI AI Hiring
FutureFit AI has 2 open AI roles right now. They're hiring across Data Scientist, MLOps Engineer. Based in New York, NY, US. Compensation range: $155K - $353K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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