We're hiring a Staff Data Scientist, Decisioning to improve the systems that determine how Pivotal makes high-value decisions in core workflows. This role is for someone with a strong applied data science or optimization background who wants to work on real production systems, not just offline analysis. You'll operate at the intersection of data, experimentation, modeling, economics, and software. The work may span areas like the offer engine, feedback loops for model and rule improvements, configurable decision systems, experimentation on operational workflows, and other products where better decision quality directly improves business outcomes. We're especially interested in candidates who have worked on hard decisioning problems in environments like marketplaces, pricing and revenue optimization, lending and credit, ad tech, bidding systems, or other domains where experimentation, optimization, and operating constraints all matter at once. This is not a research-only role. We want someone who can think deeply, work rigorously, and still move quickly from analysis and modeling into production changes with measurable impact. We also want someone who is AI-first in their own workflow. The right person will use AI actively for exploration, analysis, iteration, experimentation, and engineering support, and will help the team build a strong AI-native way of working.
What You'll Do
Improve decisioning systems that affect pricing, offer behavior, workflow routing, and other economically meaningful product outcomes.
Design and run experiments that help us understand whether product, rules, prompt, or model changes are actually improving performance.
Build models, heuristics, optimization logic, or evaluation frameworks that improve real-world outcomes under operational constraints.
Help translate business objectives into measurable decisioning systems with clear tradeoffs and success criteria.
Staff Data Scientist at Pivotal Health in Brooklyn | Findjobs
Work closely with engineers to productionize new logic rather than stopping at notebooks or offline recommendations.
Help design feedback loops that connect model and workflow behavior back to product and business outcomes.
Contribute to configurable rules systems and other controls that make decisioning easier to manage and improve over time.
Use AI as a force multiplier in your own workflow and help the team move faster by bringing strong AI-native habits.
Balance rigor and speed in an environment where shipping matters and iteration is constant. What Success Looks Like In the first 6 to 12 months, strong outcomes in this role would include:
meaningful improvements to the offer engine or related decision systems
high-quality experiments that clarify which changes actually improve outcomes
stronger feedback loops between model behavior, rule behavior, and business performance
better visibility into tradeoffs across quality, economics, operational cost, and user outcomes
becoming a trusted owner of an important decisioning surface
helping raise the team's bar on experimentation, analytical rigor, and evidence-based iteration
Who You Are
You are excited by high-stakes decisioning problems that combine data, uncertainty, incentives, and operational constraints.
You have strong applied data science instincts, but you want your work to ship.
You are comfortable moving between modeling, experimentation, analysis, and product or engineering collaboration.
You can reason clearly about metrics, tradeoffs, and second-order effects.
You are pragmatic and can choose the right level of sophistication for the problem instead of overcomplicating it.
You are an AI power user yourself and actively use AI in your day-to-day work with good judgment.
You like operating in fast-moving environments where the path is not perfectly defined and impact comes from good judgment plus speed. We'd Be Especially Excited If You Have
experience in pricing, revenue optimization, marketplace ranking, matching, bidding, lending, credit, underwriting, or ad tech systems
experience designing and interpreting experiments in production environments
experience with optimization, decisioning, forecasting, or applied statistics in high-leverage product systems
experience partnering tightly with engineers to productionize models, logic, or recommendations
experience with Python and backend-adjacent technical workflows
experience turning messy business problems into measurable systems with clear operating tradeoffs
experience in healthcare or other operationally complex industries Why This Role Is Interesting
The problems are economically meaningful and close to core company outcomes.
The work combines data science depth with real product and production ownership.
You'll have room to shape the next generation of decisioning systems at a company where these systems matter.
This is the kind of role where strong thinking can translate into clear business leverage quickly.
We care about rigor, but we also care about shipping.
Candidates from marketplaces, lending, pricing, and optimization teams often do especially well in this kind of environment because they are used to balancing models, metrics, and operational realities. Example Technical Environment Our current environment includes technologies and patterns like:
Python-based services and internal ML tooling
model evaluation and experimentation workflows
configurable rules and decisioning systems
production APIs and operational workflows
AI and model-backed systems grounded in structured internal data You do not need to match this stack perfectly, but you should be comfortable learning quickly and shipping in this kind of environment.
Benefits
Include: • Competitive compensation, including equity • Full health, dental, and vision coverage • Retirement savings plan through 401(k) • Flexible time off • Opportunities for company-wide connection and events Ready to Make an Impact?We're building something meaningful; and we want you on the team. Bring your ideas, curiosity, and drive, and let's transform healthcare reimbursement together. Employment Information Work Authorization Candidates must be authorized to work in the United States without current or future employer sponsorship.