The Core Engineering, New York, Vice President, Quantitative Engineering

Build reusable data pipelines and data products across databases, lakehouse and cloud platforms, Anaplan, and other enterprise systems; improve data quality, lineage, orchestration, and performance.

Maintain, and improve existing operational models used for business management.

Strengthen existing architectures and help new development start with production needs in mind.

Partner with modelers from prototype onward to turn complex statistical and machine learning models into reliable, maintainable products with appropriate testing, controls, monitoring, documentation, and operational support for forecasting, planning, and scenario design.

Develop and support practical models for financial planning, with focus on pipeline-style models (for e.g. headcount projections) that can serve as independent challenger models to test business forecasts and identify key assumptions, risks, and alternative outcomes.

Support production economic scenario generation and other DAG-heavy modeling workflows, making complex calculation graphs scalable, observable, reproducible, and easy for downstream teams to consume.

Apply agentic development selectively to improve data discovery, model workflows, controls, scenario analysis, and reporting, with appropriate evaluation and safeguards.

Set pragmatic technical direction and represent a central function across multiple divisions: understand distinct needs, manage senior stakeholders, mediate competing priorities, and build consensus around reusable solutions.

Review designs and code, mentor practitioners, and maintain a responsive, service-oriented partnership with Finance, Risk, Engineering, and business teams.

Bachelor’s or Master’s degree (U.S. or foreign equivalent) in a quantitative or technical field such as Statistics, Electrical Engineering / Computer Science, Applied Mathematics, Economics, Physics, or Engineering.

Eight (8) or more years of relevant experience spanning data, modeling, forecasting, financial-planning technology, or software-enabled analytical products.

A track record of taking ownership of existing operational models, maintaining and improving them for business-management use, and scaling analytical solutions for sustained production.

Candidates should be modeling and data practitioners at heart, with the software fluency to deliver.

The strongest candidates will have improved imperfect real-world models and data, solved practical numerical problems, and moved useful work from local prototypes into dependable shared solutions.

Special Skills Required to Perform the Job

The role requires strength across the following areas:

Practical Modeling: Strong quantitative judgment and an exceptional command of the practical techniques needed to make models work with messy data, limited history, changing business conditions, and operational constraints.

building independent challenger models to assess business projections, test assumptions, and explain differences is required; experience with financial-planning and driver-based models is particularly relevant.

Forecasting & Time Series: Experience building and evaluating forecasts, selecting fit-for-purpose methods, diagnosing failure modes, and handling structural change, uncertainty, and unstable real-world series.

Data Engineering & Databases: Strong SQL, data-modeling, pipeline, orchestration, quality, and lineage skills across relational, analytical, and cloud data platforms.

with lakehouse architectures, Spark, or Databricks is a plus.

Programming & Production: Advanced Python and R, sound software-development practices, and hands-on experience taking analytical workflows into scalable cloud production.

Rust is a plus.

Planning & Scenario Platforms: Experience connecting models and data to Anaplan or similar planning platforms.

scaling economic scenario generators, simulations, or other dependency-heavy calculation workflows is a major plus.

Agentic Development: Working knowledge of agentic systems, including tool integration, orchestration, evaluation, state or context management, and appropriate controls.

Cross-Divisional Leadership: Demonstrated experience operating from a central function and delivering for multiple divisions.

Strong service orientation and stakeholder-management skills, with the credibility to reconcile competing needs, mediate trade-offs, build consensus, and connect technical decisions to shared business outcomes.