About IV.AI iv.ai
IV.AI iv.ai was founded in 2016 with a focused mission: to help humans become smarter while tackling hard problems that require artificial intelligence. We pair natural language processing (NLP) models with quantitative and generative AI to solve high-stakes business problems for our clients. As a distributed and collaborative global team, we are committed to driving real, measurable value for our customers through innovative technology and exceptional service.
Why this role
You’ll serve as the technical backbone for our growing data science team. You'll bring rigorous statistics and machine learning expertise and help us evolve from notebook-based exploratory work into centralized, production-grade data pipelines. This is a hands-on individual contributor role with both ownership of process and technical mentorship responsibility.
What you'll own (and how we measure it)
Technical quality and methodology
- Bring statistical rigor and ML expertise to a team that's currently strong on exploration but light on formal methodology. Own the technical quality bar for models, metrics, and analyses across the team.
- Help define what "good" looks like technically: code review standards, reproducibility practices, and a clear path from prototype to production.
- Stay current on modern ML and agentic workflow techniques and help the team adopt them where relevant.
- Contribute hands-on to real analyses and models. This is not a purely advisory seat; it's a working technical role.
Production infrastructure
- Help migrate our work from ad hoc notebooks into centralized, version-controlled, reproducible workflows and shared artifact storage.
- Design and implement production-grade data pipelines on GCP: scheduled and triggered jobs, not just one-off notebook runs.
Mentorship and team growth
- Mentor data scientists across the team at varying experience levels, from junior to mid-level, raising the technical bar on statistics, ML, and production practices.
- Partner with the DS team's manager (SVP of Product & Data) on technical direction while they focus on enterprise strategy and vision.
The reality of this role
- Small, senior, distributed team: you help build the practice, you don't inherit one.
- This is multi-project work that varies widely in scope and domain, so you'll move between problems rather than specializing narrowly.
What we're looking for
Must-haves
- 6-8+ years of professional data science experience, with a strong formal foundation in statistics and machine learning. You're comfortable owning methodology decisions, not just running existing scripts.
- At least 2-3 years of hands-on production/deployment experience, not just modeling in isolation.
- Experience productionalizing data science work: moving from notebooks to scheduled, reliable, production pipelines.
- Hands-on experience with GCP (BigQuery, Cloud Composer, Cloud Functions, or similar); comfort designing cloud-based scheduled and triggered workflows.
- Strong Python skills across the full DS workflow: ETL, analysis, and modeling.
- Experience mentoring or technically leading less experienced data scientists.
- You've thrived in a startup, small, or other 1-10 environment where you built structure from scratch and owned the outcomes.
Helpful, not required
- Data engineering background.
- Experience with agentic or LLM workflows.
- Data analytics or BI tooling experience.
IV.AI iv.ai is an equal opportunity employer. We celebrate diversity and are committed to building an inclusive team, and we do not discriminate on the basis of race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, ancestry, age, disability, medical condition, genetic information, marital status, military or veteran status, or any other characteristic protected by applicable federal, state, or local law. IV.AI iv.ai provides reasonable accommodations to applicants and employees with disabilities. To request an accommodation in the application process, contact hiring@iv.ai.
Requirements
Must-haves
Why this role
What you'll own (and how we measure it)
Technical quality and methodology
Bring statistical rigor and ML expertise to a team that's currently strong on exploration but light on formal methodology. Own the technical quality bar for models, metrics, and analyses across the team.
Help define what "good" looks like technically: code review standards, reproducibility practices, and a clear path from prototype to production.
Stay current on modern ML and agentic workflow techniques and help the team adopt them where relevant.
Contribute hands-on to real analyses and models. This is not a purely advisory seat; it's a working technical role.
Production infrastructure
Help migrate our work from ad hoc notebooks into centralized, version-controlled, reproducible workflows and shared artifact storage.
Design and implement production-grade data pipelines on GCP: scheduled and triggered jobs, not just one-off notebook runs.
Mentorship and team growth
Mentor data scientists across the team at varying experience levels, from junior to mid-level, raising the technical bar on statistics, ML, and production practices.
Partner with the DS team's manager (SVP of Product & Data) on technical direction while they focus on enterprise strategy and vision.
The reality of this role
Small, senior, distributed team: you help build the practice, you don't inherit one.
This is multi-project work that varies widely in scope and domain, so you'll move between problems rather than specializing narrowly.
6-8+ years of professional data science experience, with a strong formal foundation in statistics and machine learning. You're comfortable owning methodology decisions, not just running existing scripts.
At least 2-3 years of hands-on production/deployment experience, not just modeling in isolation.
Experience productionalizing data science work: moving from notebooks to scheduled, reliable, production pipelines.
Hands-on experience with GCP (BigQuery, Cloud Composer, Cloud Functions, or similar); comfort designing cloud-based scheduled and triggered workflows.
Strong Python skills across the full DS workflow: ETL, analysis, and modeling.
Experience mentoring or technically leading less experienced data scientists.
You've thrived in a startup, small, or other 1-10 environment where you built structure from scratch and owned the outcomes.
Helpful, not required
Nice-to-haves
None flagged.
Application prep
Use the role’s language and application details while tailoring your materials.