Our Mission at Tactable

What if you could build software that didn’t just scale, but transformed entire organizations?

At Tactable, this is what drives us. We’re building a world-class cloud, data, and API engineering firm with a mission to power the most influential tech of tomorrow, through expert-led delivery, strong partnerships, and a relentless focus on quality.

We don’t just consult. We build.

Founded by engineers who care deeply about people, process, and product, we go beyond just solving problems. We embed with clients, work across full project lifecycles, and operate at the speed of startups while upholding the rigor of enterprise-grade engineering.

From financial institutions to emerging tech ventures, our work is behind some of the most mission-critical systems in production today, and we’re just getting started. With growing demand from top-tier clients and a strong runway for expansion, we’re building a team of curious, ambitious developers who want to build meaningful things with meaningful people.

Take a look at tactable.io to learn more about our work and what sets us apart.

The Role: Data Scientist

We are looking for a Data Scientist to guide the ethical, transparent, and compliant development of AI systems, particularly LLM and RAG-based solutions. This role focuses on ensuring AI fairness, accountability, and trustworthiness through proactive governance, best practices, and continuous monitoring of production AI systems. This includes hands-on contribution to the deployment of AI applications—from an initial business use case or prototype all the way to a production-ready system.

What You’ll Do

End-to-End Data Science + ML Development

• Build, deploy, and maintain AI/ML solutions across the full model lifecycle—from concept and prototyping to scalable, production-ready systems, with emphasis on LLM and RAG applications.
• Establish and enforce Responsible AI frameworks, ensuring fairness, transparency, bias mitigation, and compliance with data privacy and governance standards.
• Monitor and evaluate AI systems (including chatbots and LLMs) to ensure performance, reliability, safety, and ethical alignment, using both online and offline quality assessment methods.
• Detect and mitigate risks such as bias, harmful outputs, and unsafe prompt usage; develop and maintain prompt governance policies.
• Collaborate with technical, legal, and risk teams to assess data usage, model outputs, and content safety considerations.
• Drive AI ethics education, and stay current on industry standards, regulations, and best practices to continuously improve AI governance.

Technical Leadership

• Design and implement deployment pipelines that accelerate the transition from prototype to production AI systems.
• Lead key phases of the ML lifecycle, including experimentation, deployment, and monitoring.
• Build, test, and maintain predictive models and other ML components across diverse business domains.
• Integrate with enterprise AI platforms such as IBM Watson Next.
• Develop and maintain reusable components for feature engineering, model evaluation, and deployment.
• Mentor junior data scientists through code reviews, best practices, and collaborative experimentation.
• Create internal tooling and documentation to support scalable, reproducible work.
• Contribute directly to production codebases as a hands-on data scientist.

Client & Project Exposure

• Support clients in scaling AI prototypes into high-reliability deployments integrated with existing data and application ecosystems.
• Work directly with client product, engineering, and compliance teams to deliver robust, auditable AI solutions.
• Rotate across domains (e.g., finance, trading, enterprise tech) every 6–12 months, applying ML to diverse business problems.
• Lead greenfield modeling, legacy modernization, and ML-driven product development initiatives.
• Serve as a strategic partner, shaping AI/ML product roadmaps and ensuring alignment with regulatory and ethical standards.

What You Bring

Must-Have Experience

• 7+ years in AI/ML strategy, governance, or applied data science, including 2+ years in Responsible AI, model evaluation, or LLM risk management.
• Direct experience deploying AI applications, from defined use cases or by productionizing prototypes.
• Strong understanding of Responsible AI principles: fairness, accountability, transparency, explainability, and privacy.
• Hands-on experience designing evaluation frameworks for RAG or LLM systems.
• Proven work in AI use case development and monitoring live chatbot/LLM deployments.
• Familiarity with LLM evaluation and prompt governance tools (e.g., Ragas, TruLens, Giskard, OpenAI Evals) and chatbot quality metrics.
• Experience with bias detection, compliance controls, and data privacy in production environments.
• Background in building predictive models and applying statistical and mathematical analysis to real-world problems.
• Strong Python skills, including writing reusable libraries and production-quality code.
• Deep understanding of LLM architectures, prompt engineering, and evaluation techniques.
• Strong foundation in statistics, mathematics, and critical thinking for data-driven problem solving.
• Familiarity with ethical AI policy, Model/System Cards, and regulatory standards (e.g., EU AI Act, NIST AI RMF).
• Excellent technical problem-solving skills and experience collaborating across cross-functional AI/ML teams.

Bonus Points For

• Exposure to agent-based AI systems or orchestration tools (e.g., Semantic Kernel, Haystack).
• Experience taking early-stage prototypes through full deployment pipelines (CI/CD, monitoring, observability, model/feature stores).
• Familiarity with Agile/Scrum practices and tools like Jira and Confluence.
• Awareness of AI governance, bias mitigation, and Responsible AI frameworks.

Why You’ll Love This Role

• Growth-First Culture: From custom career paths to project rotation, we design roles around your goals, not just business needs.
• Full Ownership & Impact: You’ll own critical parts of delivery, architecture, and technical decision-making. No red tape, no silos.
• Tight-Knit Team: We’ve built a culture of trust, collaboration, and curiosity. Whether it’s team lunches, hack days, or a new internal tool, we move as one unit.
• Real Work, Real Users: We’re not building MVPs that sit on a shelf. You’ll work on systems that millions rely on, every day.
• Flexibility with Structure: We’re a hybrid-first team with a strong appreciation for in-office collaboration, especially at our downtown Toronto HQ. We encourage in-person presence to foster mentorship, connection, and collaboration.

Why This Might Not Be a Fit

• You’re looking for narrowly scoped responsibilities or long-term focus on a single domain
• You prefer a rigid hierarchy with formal titles and isolated workstreams
• You’re more comfortable in a vendor-style delivery model than deep technical partnerships

Compensation, Benefits & Perks

• Salary Range: Competitive and flexible, based on experience and fit for the role.
• Comprehensive health and dental plan
• Generous PTO and holidays
• Laptop & home office equipment provided
• Career coaching and personalized development plans
• Regular social events, team outings, and wellness activities

Ready to Build the Next Generation of Data Infrastructure?

No cover letter required. Just apply, and let’s start building.