Senior Data Scientist, Innovation Lab - Remote
Experian · San Diego, CA
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Jul 15, 2026
San Diego, CA
Jul 21, 2026
What this job asks for AI summary
A research and development role within an internal innovation lab, focused on building machine learning and deep learning solutions that extract value from large, varied datasets. Day-to-day work spans prototyping new data products, designing data structures, developing algorithms, and validating model performance. Suited to someone with a PhD and hands-on experience across deep learning architectures, generative AI tools, and big data frameworks such as Spark and Hadoop.
Mid level · 2+ years · Remote · Doctorate required · Full-time
Advertised as Senior, but the requirements read as Mid.
“or” means any one of them counts — you don't need all of them.
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 2,150 people nationally plausibly meet what this posting asks for (data scientists). range 640–3,900
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 (7)
The posting hard-requires a PhD degree — equivalent experience is not offered as a substitute. Combined with only 2+ years of post-degree experience, the role is classified Mid despite the 'Senior' title; the PhD gate and shallow experience floor are characteristic of a research-track mid-level hire.
Compensation figures are referenced ('base pay range for this position is listed above') but no actual numbers appear in the provided text; offered compensation fields are left unpopulated.
Deep learning architectures (CNN, RNN, LSTM, attention models) and ML methodologies (SVM, GLM, boosting, random forest) are listed as a single compound requirement in the Qualifications section; they are captured under the umbrella 'deep learning' and 'machine learning' skills rather than as individual algorithm entries, as the JD treats them as a single experience block.
PyTorch, Keras, and TensorFlow are listed together as interchangeable open-source deep learning frameworks; PyTorch is used as the primary name with Keras and TensorFlow as alternatives.
MongoDB and Cassandra are listed as NoSQL alternatives alongside Hadoop/Hive/HBase; MongoDB is the primary name with Cassandra as an alternative.
GPU programming and mobile/online marketing analytics appear at the end of the Qualifications section without 'required' framing and read as additional desirable experience rather than hard gates — listed as preferred.
Caller marked this a fully-remote role — scored against the national candidate pool.
Read the full posting
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