Best Fit
You're already writing production code. Let's build the resume that says so.
You write code at work every day. Engineering teams still don't see you as an engineer.
You're a data scientist, data analyst, business intelligence engineer, or financial analyst. You write Python, SQL, maybe R, every day, on real problems, for real stakeholders. You've built models, pipelines, and dashboards that people depend on. But when you apply to software engineering roles, you're filtered out. And when you stay in data roles, the ceiling is low. You're technically capable and professionally stuck.
Data professionals who can build are in high demand at startups, but rarely positioned to compete for engineering roles.
Challenges we help solve
- 'Data person' vs 'engineer': hiring filters don't know what category you're in
- Python and SQL are treated as analytics tools, not engineering skills
- No production software deployment experience to point to
- Engineering roles want SWE history; data roles want more analytics time. No path forward.
What you leave with
- Internship experience in a product engineering org, not a data team
- Exposure to production codebases, deployment, and team engineering workflows
- Career coaching that positions your quant depth as a product engineering asset
- A clear narrative: generalist who builds, not specialist who reports
The gap isn't technical. It's categorical.
You've been writing Python for years. You understand data at a level most junior engineers don't. But the hiring system has you filed under 'data', and that filter is hard to escape without professional experience in an engineering context. More courses won't change that.
- Analytics engineering and software engineering are treated as separate hiring tracks
- No product engineering job title means no SWE screening callbacks
- Even strong technical skills don't transfer when the professional context isn't there
Software Engineer - Backend
Series B fintech · 80–120 employees
Requirements
Strong Python programming skills
5 years Python, pandas, sklearn, NumPy
Experience with SQL and data modeling
Daily SQL, built 3 analytics schemas
Production software deployment experience
Models in notebooks and scripts, no deployed services
REST API design or backend service development
Consumed APIs, never built one
Team code review and version control workflows
Git basics, no PR review culture on data team
Technical ability: not the gap. Engineering context: is.
Engineering context is what changes how teams read your resume.
An internship in a product engineering environment, real codebase, real deployment, real code review, gives you the context that makes your quant skills land differently. Combined with coaching that repositions your narrative, it changes how you're categorized.
- Product engineering internship adds the SWE context your resume needs
- Technical mentorship on engineering norms: version control, testing, deployment
- Positioning coaching: from 'data person looking to switch' to 'engineer who builds data-intensive products'
Career Coaching - Session 1
How you position yourself · Marcus T.
Before coaching
"I'm a data scientist with 4 years of experience, looking to transition into software engineering. I know Python and SQL but I haven't done backend work."
Leads with the category. Frames the transition as a gap to overcome.
After coaching
"I'm an engineer who builds data-intensive products. I've shipped models and pipelines that real teams depend on, and I now have internship experience building the backend systems that surround them."
Leads with the role. Uses domain depth as a differentiator, not a disclaimer.
Target: product engineering at data-heavy startups and scale-ups
Sound like you? Let's talk.
The admissions project is how we get to know you. Build something real and show us what you've got.
Frequently asked questions
Common questions from quantitative professionals considering the program.
- I'm a data scientist. Isn't that already a technical role?
- Yes, and a strong one. The challenge isn't your technical ability, it's the hiring category. Software engineering teams often see 'data scientist' and assume you want to stay in data. We help you break out of that box.
- What if I want to stay in data but move into a more engineering-focused data role?
- That's a valid path too. The program helps whether you're aiming for software engineering broadly or specifically data engineering, ML engineering, or platform roles. The internship experience and coaching apply either way.
- I already have a good job. Why would I do this?
- A lot of quant professionals come in with stable jobs and a ceiling. If you're looking at your career in 3-5 years and want more options, or want to build products instead of just analyze them, the program is designed for that transition.