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. ABOUT THE ROLE As a Data Scientist at Minerva, you build the models and features that power our consumer graph and the agents that run on top of it. You sit at the intersection of heavy data engineering and applied modeling: you architect feature engineering pipelines that are computed over terabytes of data, train and sharpen the models that drive targeting and prediction, and ensure the outputs are robust enough to be consumed autonomously by our Minerva Agents and our world-class modeled attributes (i.e. income / wealth). This is a role that will be deploying constantly to production. The models you build are not handed off to be deployed by someone else, you own the path from raw data to a feature or model that an agent can call reliably at scale. As we grow, your work becomes the foundation other systems are built on. WHAT YOU'LL DO - Create new features for models and agents, expanding the predictive surface area of our consumer data lake and building the pipelines that turn raw signal into trusted attributes. - Improve existing models through rigorous feature engineering, including our income/wealth, home buyer, and home seller models. - Play a pivotal role in the buildout of our world-class data lake, shaping how terabytes of consumer data are stored, transformed, and made queryable for both humans and agents. - Build feature engineering pipelines that run efficiently at terabyte scale, with the data engineering rigor to make them reliable in production. This is a 70/30 split DS/DE role. - Ensure model and feature outputs are reliable enough to be consumed agentically, writing the validations and guardrails that let our agents act on your work without a human in the loop. OUR DATA STACK - Dagster for all things orchestration - dbt-core within Dagster as the primary data transformation surface - Spark, Iceberg, Trino, AWS Glue for Lakehouse workloads - Modal for ML eng - Frontier + OSS models & agent SDKs. We are heavy users of OpenAI/Anthropic batch APIs QUALIFICATIONS - 2-4+ years working as a data scientist, applied machine learning focused data engineer or software engineer in a data-heavy context. Simply put, you live and breathe data. - Highly proficient at Python and SQL. - You are driven by first-principles thinking and are a go-getter. You reason about what datasets and features are necessary to solve a modeling problem, and are scrappy and clever enough to bring that to life. - Strong intuition for data engineering principles, especially around data cleaning/ingestion and data modeling. We prefer these core skills to be second-nature, freeing up thinking for architecting and executing large-scale data initiatives, especially given the advancement of AI coding tools. - Strong engineering background. You are comfortable deploying complicated production pipelines and working within larger production systems, not just in sandboxed or research environments. - Willingness to work in office in NYC (we provide a relocation package). - Flexibility and openness to wearing several hats. We are lean and things are always changing. - Eagerness to learn and grow with the company and your coworkers. PREFERRED - Experience building and training predictive models (e.g. lead scoring, LTV, propensity, lookalike modeling). - Experience with orchestration tools like Dagster, Airflow, Prefect and SQL transformation tools like dbt, SQLMesh. - Experience with both transactional databases (e.g. Postgres, MySQL) and analytical databases (e.g. Snowflake, Redshift), with a bias toward the latter. - Familiarity with a cloud resource provider (e.g. AWS, GCP). - Familiarity with backend and ML/AI engineering. - Experience with AI coding tools (e.g. Cursor, Claude Code, OpenCode) as a force multiplier. - Prior work at an early-stage startup. You don't need to tick every box. If you're strong on the engineering side and hungry to build models that matter, we want to hear from you. COMPENSATION $160,000–$230,000 base salary, commensurate with experience. Competitive equity. Medical, dental and vision. WHERE WE WORK We work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn. BENEFITS Every full-time role includes equity; medical, dental and vision coverage; unlimited PTO; a monthly fitness stipend; and an annual company offsite. We'd rather pay people well than buy perks nobody uses. PAY TRANSPARENCY The salary range in this posting is our good-faith range for the role in New York City at the time of posting. Where an offer lands depends on the level and experience we see in the interview process. APPLYING Apply on this page. We don't take applications by email, and we don't accept unsolicited submissions from recruiting agencies. If you need an accommodation at any point in the process, write to hiring@minerva.io. WORK AUTHORIZATION You'll need to be authorized to work in the United States. We consider visa sponsorship case by case, so tell us on the application if you'll need it. EQUAL OPPORTUNITY Minerva is an equal opportunity employer. We make hiring decisions without regard to race, color, religion, creed, sex, pregnancy, sexual orientation, gender identity or expression, national origin, citizenship status, age, disability, marital status, veteran status or any other characteristic protected by federal, New York State or New York City law. RECRUITMENT FRAUD Our hiring runs through Ashby and @minerva.io email addresses. We will never ask you for money, bank details or other financial information during the process. If a message claiming to be from Minerva looks wrong, forward it to hiring@minerva.io.

Requirements

Must-haves

Create new features for models and agents, expanding the predictive surface area of our consumer data lake and building the pipelines that turn raw signal into trusted attributes.
Improve existing models through rigorous feature engineering, including our income/wealth, home buyer, and home seller models.
Play a pivotal role in the buildout of our world-class data lake, shaping how terabytes of consumer data are stored, transformed, and made queryable for both humans and agents.
Build feature engineering pipelines that run efficiently at terabyte scale, with the data engineering rigor to make them reliable in production. This is a 70/30 split DS/DE role.
Ensure model and feature outputs are reliable enough to be consumed agentically, writing the validations and guardrails that let our agents act on your work without a human in the loop.
Dagster for all things orchestration
dbt-core within Dagster as the primary data transformation surface
Spark, Iceberg, Trino, AWS Glue for Lakehouse workloads
Modal for ML eng
Frontier + OSS models & agent SDKs. We are heavy users of OpenAI/Anthropic batch APIs
2-4+ years working as a data scientist, applied machine learning focused data engineer or software engineer in a data-heavy context. Simply put, you live and breathe data.
Highly proficient at Python and SQL.
You are driven by first-principles thinking and are a go-getter. You reason about what datasets and features are necessary to solve a modeling problem, and are scrappy and clever enough to bring that to life.
Strong engineering background. You are comfortable deploying complicated production pipelines and working within larger production systems, not just in sandboxed or research environments.
Willingness to work in office in NYC (we provide a relocation package).
Flexibility and openness to wearing several hats. We are lean and things are always changing.

Nice-to-haves

Experience building and training predictive models (e.g. lead scoring, LTV, propensity, lookalike modeling).
Experience with orchestration tools like Dagster, Airflow, Prefect and SQL transformation tools like dbt, SQLMesh.
Experience with both transactional databases (e.g. Postgres, MySQL) and analytical databases (e.g. Snowflake, Redshift), with a bias toward the latter.
Familiarity with a cloud resource provider (e.g. AWS, GCP).
Familiarity with backend and ML/AI engineering.
Experience with AI coding tools (e.g. Cursor, Claude Code, OpenCode) as a force multiplier.
Prior work at an early-stage startup.
You don't need to tick every box. If you're strong on the engineering side and hungry to build models that matter, we want to hear from you.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiawsmlmachine learningpythonsqlgopostgresgcp

Platform: ashby
Effort: quick

Benefits

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Dental

Vision

Pto