below marketmost similar roles pay hereabove market
This role pays more than
91%
of similar roles. Most pay
$156,875–$231,625
— the blue band above.
At the midpoint, this role pays about
$279k
versus about
$194k
for comparable roles.
Based on 240 similar postings.
Employer
About Reddit
Reddit is a social news aggregation and discussion platform where users share content, vote on posts, and engage in community conversations across thousands of interest-based forums called subreddits.
Reddit currently has
92 open roles
on FindRole.
Listed pay typically runs
$216,700–$303,000
across 92 roles with salary data.
JOB TITLE: Senior Staff Applied Scientist, Science Platform
The Senior Staff Applied Scientist joins the Data Science Platform team to establish the scientific direction for agentic analytics, causal inference, and forecasting. This role involves identifying research problems, implementing emerging methods, and shipping production capabilities that enable a scientific understanding of human activity. You will design and build an analytics agent capable of decomposing questions, selecting scientific methods, and communicating findings with clear uncertainty. Key responsibilities include developing reusable causal inference tools for observational data, building shared forecasting capabilities for business metrics, and creating evaluation frameworks for agent-generated analyses. Required skills include deep expertise in statistics, econometrics, and machine learning, alongside strong software engineering abilities in Python. You will translate complex research into reliable software, APIs, and data pipelines to solve practical business problems.
What does a Applied Scientist earn?
Median $199700 from 50 postings across 11 companies.
Define the scientific roadmap for agentic analytics, causal inference, and forecasting across the Data Science Platform.
Research and implement emerging methods by moving quickly from research papers to production-ready prototypes.
Design the architecture for an analytics agent to decompose questions, select methods, and produce reproducible results.
Build reusable causal inference tools to estimate the effects of product changes and business decisions using observational data.
Develop shared forecasting capabilities including probabilistic predictions, hierarchical forecasts, and scenario analysis.
Create evaluation frameworks and benchmarks to measure the correctness and usefulness of agent-generated analyses.
Write robust Python code to implement scientific methods into production software, APIs, and data pipelines.
Translate recurring analytical needs from business partners into accessible platform capabilities and workflows.
What we're looking for
A doctorate in Statistics, Economics, Computer Science, Applied Mathematics, Operations Research, or a related discipline; alternatively, a master’s degree with comparable depth of expertise.
At least eight years of relevant industry work following a doctorate, or ten years with a master’s degree, in applied science, data science, or scientific software development.
Deep knowledge of statistical inference and econometrics with practical experience developing causal methods for observational data.
Experience developing and deploying forecasting systems, including time series modeling, temporal validation, and prediction intervals.
Strong software engineering skills with substantial experience writing and maintaining production Python software, including API design and automated testing.
Experience developing LLM applications or analytical agents, including tool use, code execution, and systematic evaluation of outputs.
Demonstrated ability to critically assess scientific literature and turn promising methods into reliable software that solves practical problems.
Experience developing other technical contributors through mentorship, design reviews, and collaboration across organizational boundaries.
Python
PyTorch
Generative AI
Diffusion Models
VLM
Distributed Training
Multi-modality
Deep Learning
Data Curation
Captioning Pipelines
Image-to-Image
Mid-training
Pre-training
Post-training
Deep Learning
Large Language Models
Python
PyTorch
TensorFlow
C++
C#
Java
C
NLP
Distributed Training
Machine Learning
Predictive Analytics
Search
Document Understanding
High Throughput Systems
Generative AI
Large Language Models
Machine Learning
Natural Language Processing
Deep Learning
Synthetic Data Generation
Responsible AI
Inference Pipelines
Experimentation Frameworks
Privacy-Preserving ML
Computer Vision
Recommender Systems
Statistics