Dataset Lead, Data Science & Analytics

Reinforced Learning; Town of Tonawanda (NY); On-site; USD 90,000 - 130,000; Full time

Job summary

Reinforced Learning seeks a data scientist to investigate metric drops, validate tracking changes, and reproduce findings on held-out data. You’ll build datasets that teach models to analyze with evidence behind every conclusion.

You’ll work with a contributor bench of data scientists, use realistic warehouses and notebooks as fixtures, and follow rubrics that reward reproducibility while penalising unsupported claims, with reviews feeding back into task design.

Qualifications

• Four or more years in data science or analytics with hands-on SQL and Python.
• You’ve been wrong about data before and know how it happens.
• Strong writing and experience mentoring or reviewing analysts.
• Interest in how models reason about data.

Responsibilities

• Investigating a metric drop, validating a tracking change, reproducing a finding on held-out data.
• You’ll build datasets that teach models to do analysis the way careful analysts do, with evidence behind every conclusion.
• Rubrics that reward reproducibility and penalise unsupported claims.
• Review of delivered work, with findings fed back into task design.

Skills

SQL

Python

Data analysis

Job description

The role

Investigating a metric drop, validating a tracking change, reproducing a finding on held-out data. You’ll build the datasets that teach models to do analysis the way careful analysts do, with evidence behind every conclusion.

What you’ll own

• Task families across SQL analysis, experimentation, modelling and reporting.
• A contributor bench of data scientists and analysts.
• Realistic warehouses, event logs and notebooks as task fixtures.
• Rubrics that reward reproducibility and penalise unsupported claims.
• Review of delivered work, with findings fed back into task design.

What you bring

• Four or more years in data science or analytics with hands-on SQL and Python.
• You’ve been wrong about data before and know how it happens.
• Strong writing and experience mentoring or reviewing analysts.
• Interest in how models reason about data.

Helpful, not required

• Experimentation platforms or causal inference.
• Teaching material or assessments you’ve built.