Data Scientist
Neara · New York, NY
$160,000–$190,000
Jul 19, 2026
New York, NY
Jul 21, 2026
What this job asks for AI summary
This role involves building and refining machine learning models that underpin digital twin representations of electricity grids, drawing on data sources such as LiDAR, aerial imagery, and GIS. Day-to-day work spans designing data pipelines, running experiments, surfacing analytics like wildfire risk, and advising on data strategy. It suits a data or ML scientist with several years of experience in technical, data-heavy environments.
Mid level · 3+ years · Remote
Posted 2 times — it's one opening, so apply once.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Roughly 11,700 people nationally plausibly meet what this posting asks for (data scientists). range 4,900–17,500
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $122,874 (middle half $87,544–$162,374).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (7)
The JD states 3–6 years of experience; the overall years minimum is set to 3 (the lower bound). The 6-year ceiling and the role's scope (mentoring, advising on strategy, owning problems) are consistent with a solid Mid-level band; the title carries no explicit seniority label.
Python is not explicitly named in the requirements but is the overwhelmingly standard language for the data/ML stack described (Spark, Databricks, ML model training); it is inferred as a hard gate. If the hiring team uses a different primary language, this should be verified.
The data storage and ETL technologies (Parquet, Databricks, Snowflake, PostgreSQL, Spark, DynamoDB) are listed together under the 'Who You Are' requirements section with the framing 'such as', indicating named examples of a required capability class. Each is treated as a hard gate per the substitution-qualifier rule, with the understanding that equivalent tools in the same category may be acceptable.
LiDAR and GIS appear in the requirements section but are framed as part of the data pipeline context ('a variety of data sources, including LiDAR, aerial photography, photogrammetry and GIS') rather than as standalone skill gates; they are retained as preferred given the 'including' framing and the fact that geospatial experience is separately called 'a plus'.
Geospatial data experience and energy industry experience are explicitly called 'a plus' — both are preferred, not required.
Compensation is described qualitatively as 'highly competitive' with 'significant equity' but no figures are given; no comp fields are populated.
Caller marked this a fully-remote role — scored against the national candidate pool.
Read the full posting
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