Forward Deployed Engineer

Minerva; New York (NY); On-site; USD 215,000 - 241,000; Full time

Job summary

Minerva seeks a data scientist or data engineer to ship production pipelines and models for enterprise accounts in the New York area. You’ll build custom ingestion, modeling, and scoring solutions leveraging your experience with Python, SQL, dbt, and orchestration tools.

You’ll collaborate with the CTO and client teams to ensure scalable outcomes. You’ll own data readiness, develop predictive models (propensity, LTV, lookalike), and run production scoring.

Qualifications

• 4+ years as a data scientist or data engineer shipping to production in a data-heavy setting.
• Strong Python and SQL, experience with dbt and an orchestrator like Dagster or Airflow, and a cloud data warehouse (Snowflake, BigQuery or Databricks).
• Built predictive models such as propensity, LTV, lookalike or lead scoring.
• Identity resolution, entity matching, consumer or behavioral data.
• Experience working directly with customers or non-technical stakeholders.
• Willingness to relocate to New York City and travel to customers.

Responsibilities

• Turn data into production-ready pipelines and models for enterprise accounts.
• Build and deploy predictive models and data pipelines for customers.
• Run scoring in production and own model accuracy.
• Scope and build custom pipelines when standard ones don’t cover the need.
• Standardize custom solutions so they can be productized by the data platform team.
• Be the technical voice in conversations with customer data teams.

Skills

Python

SQL

dbt

Dagster

Airflow

Snowflake

BigQuery

Databricks

Data model delivery

Tools

Python tooling

SQL tooling

Job description

ABOUT MINERVA

Minerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.

Customers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.

We've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.

HOW WE DEPLOY

• Every enterprise account moves from a data test to a pilot to production. The technical win usually decides the deal.

• Every enterprise account has a team. The account executive owns the deal, and the account manager owns the relationship and the renewal after it. A forward deployed marketer owns the pilot's outcome. A solutions engineer runs the technical evaluation and gets standard accounts live on our product. A forward deployed engineer builds what our product can't do yet.

THE ROLE

Our solutions engineers get standard accounts live on our product. Some accounts need more than the product does today. A pro sports team with ten years of ticketing data in three systems. A brand that needs a model we haven't built yet. First-party data that needs real work before anyone can trust it.

Those accounts get you. You're a data scientist who ships production pipelines, or a data engineer who builds models, embedded with our most complex customers. You build the custom onboarding, pipelines and models that make Minerva work in their world, then bring what you learned home so the next customer gets it out of the box.

You work closely with our CTO and data science team, alongside each account's AE, forward deployed marketer and solutions engineer.

WHAT YOU'LL DO

• Get the data ready. Build custom ingestion and pipelines for accounts whose data doesn't fit standard onboarding, and make the call on whether the data is right before anyone builds on it.

• Build custom models. Propensity, lookalike and scoring models trained on a customer's first-party data and our consumer graph, when the standard models aren't enough.

• Run scoring in production for your accounts, and own its accuracy.

• Build the net-new. When an expansion needs something we don't have, scope it with the forward deployed marketer and build it.

• Make the one-offs standard. Turn custom pipelines and models into patterns our data platform team can productize.

• Be the technical voice in the room when a customer's data team wants to know how it actually works.

WHAT YOU HAVE

• 4+ years as a data scientist or data engineer shipping to production in a data-heavy setting: models that ran on real data, pipelines other people depended on.

• Strong Python and SQL, experience with dbt and an orchestrator like Dagster or Airflow, and a cloud warehouse (Snowflake, BigQuery or Databricks).

• You've built predictive models such as propensity, LTV, lookalike or lead scoring, and you know how to tell a customer when their data won't support one.

• Messy, multi-source data doesn't scare you: identity resolution, entity matching, consumer or behavioral data.

• You've worked directly with customers or non-technical stakeholders, and they asked for you again.

• You're in New York City or willing to relocate, and comfortable traveling to customers.

NICE TO HAVES

• Consumer data, identity graphs, adtech or martech.

• Experiment design: holdouts and lift measurement.

• Forward deployed, solutions or consulting experience at a data company.

• Sports, financial services or travel, where our enterprise motion is strongest.

WHAT THIS ISN'T

• Standard onboarding. Our solutions engineers get standard accounts live on our product. You come in when an account needs something built, before or after the deal closes.

• A research role. Everything you build runs for a paying customer.

WHAT SUCCESS LOOKS LIKE

• First 90 days: you've taken one complex account from raw data to production scoring. Your data-readiness call is written down, and the customer's data team is on your side.

• First 6 months: time-to-data-ready on complex accounts has a number, and it's falling. At least one custom pipeline or model has become a standard pattern.

• The numbers you own: time-to-data-ready and data quality on your accounts.

COMPENSATION

$215,000–$241,000 base salary, depending on experience, plus a variable bonus targeted at 10% of base. Meaningful equity. Medical, dental and vision.

WHERE WE WORK

We work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn.