Senior Data Scientist, Revenue Operations

Purpose Unlimited  is an independent financial services company with an unrelenting focus on customer-centric innovation, delivered through technology-driven solutions. Led by entrepreneur Som Seif, the company is developing a diversified product platform aimed at addressing historically underserved segments of the market. Purpose Unlimited’s businesses include Purpose Investments, Advisor Solutions by Purpose, and Driven.
Vacancy Status:
This is for a current vacancy
Compensation: $180,000 - $200,000 annual salary
Revenue is a system. You will be one of the early data science hires embedded in Revenue Ops, focused on Sales and Marketing problems across the full funnel. You will design models, measurement, and data products that help leadership make decisions with fewer opinions and more evidence.
You’ll map how revenue operations work end-to-end, translate that into a small set of metrics that steer behavior, and build quantitative models and measurement that drive better decisions. You’re an engineer, and you’ll work closely with data engineers, analysts, and software engineers so what you build is reliable and production-ready.

Note:

We use Object Process Modeling (OPM) for system mapping and Figures-of-merit (FOMs) as the measurement layer. You don’t need to be a process mapping academic, but you do need to think in systems and enjoy turning ambiguity into a measurable operating model.
Responsibilities:
What you will do   Work with product, other engineers and subject matter experts to map the revenue system (funnels, handoffs, feedback loops) and identify bottlenecks and leverage points.
Define and operationalize FOMs that connect activity to outcomes (ex. conversion, velocity, cycle time, capacity utilization, channel efficiency).
Build models that change outcomes: propensity and scoring, segmentation, time-to-event, anomaly detection, forecasting inputs.
Apply AI where it creates lift: turn unstructured signals (notes, emails, transcripts) into inputs, build retrieval + classification for “what happened and why,” and automate recurring analysis with guardrails.
Measure impact with rigor: experiments, causal inference, and incrementality measurement when attribution is misleading.
Ship decision tools (dashboards, monitoring, alerting, web-apps) and product ionize systems with other engineers (data, software, analytics).

Qualifications:

What you will bring   Have a degree in a quantitative field (BSc, MSc, or PhD in science or engineering) and have experience in highly quantitative roles.
Think in systems and enjoy turning messy reality into a model you can test, monitor, and improve.
Are strong in Python and SQL, and can own work end-to-end from raw data to deployed software.
Have built and evaluated predictive models in production-like settings (classification/ranking, calibration, thresholding, cost-sensitive decisions, decision algorithms, system dynamics).
Can measure impact with discipline, using experiments and causal methods (matching/propensity, doubly robust estimators, diff-in-diff, strong quasi-experiments).
Can apply modern AI thoughtfully, and you know how to evaluate it (offline metrics, calibration, drift, and measured impact on outcomes).
Have worked with messy operational data (CRM, marketing automation, web analytics, call/meeting signals) and made it trustworthy.
Are an engineer and work naturally with data engineers and software engineers to ship durable outputs (pipelines, scoring, monitoring).
Communicate clearly, including uncertainty and trade-offs, and you have a high bar for accuracy.
You could be an especially great fit if   You’ve been early on a team and enjoy building the measurement and modeling foundations from scratch.
You move fast without breaking things that matter, and you’re comfortable owning ambiguous problems end-to-end.
You’re the kind of teammate who makes cross-functional work feel easy: crisp updates, clear explanations, no surprises.
You’re genuinely excited about data, technology and AI, and you bring taste and discipline to how it gets applied.
You think from first principles: start with the real operational flow and user needs, then design metrics, models, and…