Data Science Manager

You get energy when you see someone on your team crack a problem they couldn’t have solved six months ago because you helped them get there. You care about technical rigor, but you care even more about outcomes. You know that a well-scoped project with clear success criteria will beat a brilliant but ambiguous one every time. And you’re not afraid to roll up your sleeves and lead the hard modeling work yourself when the situation calls for it.

At Fixify, we’re building an AI-native IT automation platform where data science sits at the center of the product, not bolted on. Our models run in production. Our experiments drive real decisions. And the team’s work directly shapes how customers experience Fixify. As a Data Science Manager, you’ll own both the development of the team and the quality of its output — ensuring research translates into reliable, production-ready systems that Product, Engineering, and Customer Success depend on.

This is a player-coach role. You’ll lead and grow a team of data scientists while staying close to complex research, modeling and experimental design. You’ll set the standards for how work gets done — from experimentation practices to model handoff — and you’ll be the person who unblocks the team when things stall. If you want to build a team that ships with confidence and grows while doing it, this is the role.

• Give you a team of data scientists to grow and a mandate to set the standard for how data science gets done at Fixify.
• Put you at the intersection of Product, Engineering, and Customer Success, where your team’s work directly shapes the product.
• Let you stay hands-on technically — this is not a step-away-from-the-work role. You’ll lead complex research alongside your team.
• Surround you with peers and leaders who value rigor, clarity, and shipping work that drives measurable impact.
• Give you the autonomy to design and refine processes and practices as Fixify scales.

• Lead, mentor, and manage a team of data scientists — setting expectations, giving feedback, and building people up.
• Own execution quality for data science delivery, ensuring research translates into production-ready, well-documented systems.
• Partner with Product to define research priorities and with Engineering to ensure models integrate cleanly into production.
• Step into complex modeling and research efforts yourself when senior technical judgment and experience are required.
• Build and improve the team’s processes — including experimentation standards, methodology reviews, documentation practices, and model handoff workflows.

• Experience leading and developing data scientists, including coaching, performance feedback, and career progression.
• Expert-level knowledge of statistical modeling, machine learning, and GenAI techniques.
• Strong proficiency in Python and SQL, with deep fluency in libraries like Pandas, NumPy, and scikit-learn.
• Experience shipping models to production and a clear understanding of what it takes to make research work reliably at scale.
• A track record of designing evaluation frameworks, monitoring systems, and logging strategies for production models.
• The ability to scope ambiguous problems, prioritize effectively, and keep a team aligned in a fast-moving environment.
• Clear communication skills — you can translate between data science, product, and engineering without losing the thread.
• Experience working cross-functionally with Product, Engineering, and business stakeholders.
• Comfort operating with ambiguity and shifting priorities; you lead through it rather than waiting for it to resolve.
• A genuine belief that quality, reproducibility, and rigor aren’t nice-to-haves — they’re how you build trust in data science.