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About This Role
Data Scientist II, Deep Learning & Image Analysis
The Allen Institute accelerates science for a healthier world through large-scale research designed to answer some of the most complex questions in biology. Our multi-disciplinary teams generate foundational knowledge, tools, and data to understand how our brain, cells, and immune system work. We share our work openly so others can build on it, move faster, and ask bigger questions. We drive discovery forward and create new possibilities for improving human health.
The goal of Cellscapes is to create the first holistic framework to predict how cells self-organize into complex cell communities and tissues in order to program an entirely new kind of synthetic tissue that can be used to ask focused questions about how cells behave, including in response to drug interventions.
The Computational Cell Science team develops scalable, quantitative image-based frameworks for analyzing cell organization, activity, and function. As a Data Scientist I specializing in computer vision, you will work alongside experimental and computational scientists, developing and implementing machine learning and deep learning methods for 3D timelapse microscopy images and owning defined components of the analysis pipeline end to end. Technical decisions within those components are yours, with guidance from senior team members on scope and direction.
The role sits where microscopy, stem cell biology, machine learning, and scientific software meet daily. You will work with large-scale microscopy datasets, applying and adapting state-of-the-art models on dedicated HPC computing infrastructure. What the team builds is released openly and used by researchers outside the Institute.
At the Allen Institute, we believe that science is for everyone – and should be open to everyone. We are dedicated to combating biases and reducing barriers to STEM careers more broadly.
We also believe that science is better when it includes different perspectives and voices. We strive to make the Allen Institute a place where everyone feels like they belong and are empowered to do their best work in a supportive environment.
We are an equal-opportunity employer and strongly encourage people from all backgrounds to apply for our open positions.
Essential Functions
- Design and implement ML/DL pipelines for microscopy image analysis, in collaboration with senior data engineers and scientists
- Preprocess and clean large-scale microscopy datasets, ensuring data quality and integrity for downstream analysis
- Develop, optimize, and fine-tune models, from classical algorithms through DL architectures, for standard image processing tasks such as segmentation, feature extraction, and classification
- Integrate and scale image analysis protocols into high-throughput computational pipelines, working with cross-functional teams
- Participate in code reviews and contribute to efficient, maintainable, well-documented codebases
- Maintain reproducible analysis records; version-controlled code, documented parameters, traceable results, and coordinate with experimental scientists on data collection and study design
- Track developments in machine learning and image analysis and evaluate promising methods against our data
- Adherence to SOPs, GLPs and regulatory requirements
Note: Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. This description reflects management’s assignment of essential functions; it does not proscribe or restrict the tasks that may be assigned.
Required Education and Experience
- Bachelor's degree in computer science, engineering, physics, data science, computational biology, or a related quantitative field
- 1+ year of relevant experience
- Demonstrated experience developing ML/DL models for segmentation, classification, or feature extraction; from research, internship, coursework, or professional work
- Proficiency in Python, including scientific and data manipulation libraries (e.g. NumPy, scikit-image)
- Working experience with a deep learning framework such as PyTorch or TensorFlow
Preferred Education and Experience
- Master’s degree with 2+ years of relevant experience in computer science, engineering, physics, data science, computational biology, or a related quantitative field; or a Bachelor's degree with 4+ years of relevant experience in one of these fields
- Experience with microscopy image data, particularly 3D or timelapse
- Experience with classical machine learning methods (e.g. SVM, random forests) alongside deep learning approaches
- Experience developing or contributing to open-source scientific tools or packages
- Familiarity with software engineering practice: version control with Git, testing, code review, and reproducible build or environment management
- Experience running analyses on HPC or distributed computing infrastructure
- Experience working in a multidisciplinary research environment spanning computational and experimental work
Physical Demands
- Fine motor movements in fingers/hands to operate computers and other office equipment
Position Type/Expected Hours of Work
- This role is currently working onsite and is expected to work onsite for the majority of working hours. The primary work location for this role is 615 Westlake Ave N., with the flexibility to work remotely on a limited basis.
Additional Comments
- Please note, this opportunity does not offer relocation assistance
- Please note, this opportunity does not offer work visa sponsorship
Annualized Salary Range
$112,950 - $139,750 *
- Final salary depends on the required education for the role, experience, level of skills relevant to the role, and work location, where applicable.
Benefits
- Employees (and their families) are eligible to enroll in benefits per eligibility rules outlined in the Allen Institute’s Benefits Guide. These benefits include medical, dental, vision, and basic life insurance. Employees are also eligible to enroll in the Allen Institute’s 401k plan. Paid time off is also available as outlined in the Allen Institutes Benefits Guide. Details on the Allen Institute’s benefits offering are located at the following link to the Benefits Guide: https://alleninstitute.org/careers/benefits.
It is the policy of the Allen Institute to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state or local law. In addition, the Allen Institute will provide reasonable accommodations for qualified individuals with disabilities
Salary Context
This $112K-$139K range is in the lower quartile for Data Scientist roles in our dataset (median: $167K across 197 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 10,565 AI roles we're tracking, Data Scientist positions make up 5% of the market. At ALLEN INSTITUTE, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($126K) sits 34% below the category median. Disclosed range: $112K to $139K.
Across all AI roles, the market median is $213,300. Top-quartile compensation starts at $264,195. The 90th percentile reaches $320,000. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,300; VP: $235,000.
ALLEN INSTITUTE AI Hiring
ALLEN INSTITUTE has 1 open AI role right now. They're hiring across Data Scientist. Based in Seattle, WA, US. Compensation range: $139K - $139K.
Location Context
AI roles in Seattle pay a median of $226,100 across 973 tracked positions. That's 6% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 10,565 open positions tracked in our dataset. By seniority: 217 entry-level, 5,017 mid-level, 3,793 senior, and 1,538 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (610 positions). The remaining 9,876 roles require on-site or hybrid attendance.
The market median for AI roles is $213,300. Top-quartile compensation starts at $264,195. The 90th percentile reaches $320,000. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 10,565 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (7,941), AI Software Engineer (498), Data Scientist (479). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (217) are outnumbered by mid-level (5,017) and senior (3,793) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,538 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (610 positions), with 9,876 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,300. Top-quartile roles start at $264,195, and the 90th percentile reaches $320,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,826 postings), Aws (1,033 postings), Azure (815 postings), Rag (713 postings), Gcp (581 postings), Pytorch (568 postings), Kubernetes (446 postings), Claude (413 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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