Data Scientist
Gradera Inc.
$100,000–$145,000
Jul 24, 2026
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Jul 25, 2026
What this job asks for AI summary
A full-lifecycle data science role covering everything from exploratory analysis and data quality auditing to building and deploying machine learning models (regression, classification, clustering, NLP, time-series). Day-to-day work spans cleaning and profiling messy real-world datasets, designing experiments, and creating self-serve analytics tools, with collaboration across data engineering and business teams on cloud and big-data platforms.
Mid level · Remote
“or” means any one of them counts — you don't need all of them.
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 480 people nationally plausibly meet what this posting asks for (data scientists). range 290–720
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
Rare in this occupation — lead with these, and say what you built with them.
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 (8)
No location is specified anywhere in the posting; remote status is inferred from the absence of any office or geography requirement and the nature of the firm (AI-native services). Treat location and remote flag as uncertain.
No total years of experience are stated; seniority is assessed as Mid based on the scope of responsibilities (full data lifecycle, cross-functional collaboration, model deployment) without explicit senior-level ownership or leadership signals.
The posting lists Python and R as alternatives ('Python or R'), with the Python library stack (pandas, NumPy, scikit-learn, PyTorch/TensorFlow) called out explicitly — those libraries are captured as separate required skills under the same Required section.
DB2 and SQL Server appear together in the Required section as specific database platforms alongside the general SQL requirement; both are captured as hard gates.
The cloud platform requirement is framed as 'Azure or AWS' — captured as a single required skill with Azure as primary and AWS as alternative.
MLOps tools (MLflow, Kubeflow, Airflow) and streaming technologies (Kafka, Spark) are listed under the Required Technical Skills section with 'such as' / 'or' qualifiers, indicating the specific tool is interchangeable but the capability is required.
Deep learning, NLP, computer vision, and Bayesian methods appear under an explicit 'Nice to Have' section and are marked preferred accordingly. Real-time/streaming pipelines and open-source contributions also appear there but name no distinct technology beyond what is already captured.
No compensation, degree requirement, employment type, or security clearance is mentioned.
Read the full posting
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