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About This Role
Robots \& Pencils is an applied AI engineering firm building the next frontier of business architecture. We design and ship AI co\-workers that integrate into enterprise operations and deliver measurable results for our clients. We're all in on AWS, combining deep UX capability with senior engineering talent to get AI into production fast and keep it there.
We've earned the trust of leaders across Consumer Products and Retail, Education, Energy, Financial Services, Healthcare, and Manufacturing and more, and earned a reputation as the nimble alternative to traditional global systems integrators. Founded in 2009, with delivery centers in Canada, the United States, Eastern Europe, and Latin America, we are smaller, faster, and more senior by design. Our teams average 15\+ years of experience. We move fast, sweat the details, and build things that actually ship.
Position Overview
We're looking for a Principal Data Scientist to join a multi\-disciplinary team focused on designing and implementing scalable and reliable approaches to support or automate decision making throughout the business. This role is ideal for an experienced data scientist who can apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear.
In this role, you will acquire data by building the necessary SQL / ETL queries and import processes through various company specific interfaces for accessing S3, RedShift, and Spark storage systems. You'll build relationships with stakeholders and counterparts, analyze data for trends and input validity, and implement models that comply with evaluations of computational demands, accuracy, and reliability.
Why This Role Matters
At Robots \& Pencils, we design AI systems for a human world. Our name says it all. Robots and pencils means engineering paired with creativity, because every agent we ship has to work for real people in real workflows. That balance is baked into how we operate.
Every role here contributes directly to that mission. Here, you shape how AI systems integrate into enterprise operations, how teams move at real velocity, and how products create measurable impact for clients and the people they serve. We ship production\-ready AI in 30 to 45 days. That pace demands people who take ownership, lead with craft, and care deeply about what they put their name on.
What You'll Do
*Craft \& Delivery*
- Design and implement scalable and reliable approaches to support or automate decision making throughout the business
- Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear
- Acquire data by building the necessary SQL / ETL queries and import processes through various company specific interfaces for accessing S3, RedShift, and Spark storage systems
- Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies
- Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks
- Validate models against alternative approaches, expected and observed outcomes, and other business defined key performance indicators
- Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production
- Implement and deploy state of the art machine learning algorithms under Gen AI, build prototypes, troubleshoot customer issues, and explore new solutions
- Interact closely with customers and with the academic community to drive innovation and deliver tailored data science solutions
*Collaboration \& Communication*
- Build relationships with stakeholders and counterparts to understand business needs and translate them into data science solutions
- Collaborate closely with engineering, analytics, AI, and product teams to align data science models and insights with broader business goals
- Communicate findings and model results clearly to non\-technical executive audiences, ensuring insights are actionable and understood
*Leadership \& Influence*
- Establish data science best practices and modeling standards that lift the quality and consistency of analytical work across the team
- Mentor junior and mid\-level data scientists, helping them grow their craft, confidence, and impact
- Bring an AI\-forward mindset to your daily work, using tools like Claude, Cursor, and other modern AI assistants to deliver higher\-quality work at pace
What You'll Bring
- 10\+ years of data scientist experience with a proven track record of solving complex business problems through data science
- Bachelor's degree and 8 years of experience or Master's degree and 4 years of experience
- 2\+ years of hands\-on experience with generative AI technology
- 5\+ years of experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high\-performance computing, neural deep learning methods and/or machine learning (Will help Win Predictor Use case)
- 5\+ years of hands on experience with Python to build, train, and evaluate models
- Customer facing experience
- Excellent communications skills with non\-technical executive audiences, with the ability to translate complex models and findings into clear, actionable insights
- Competency in data querying languages (e.g. SQL) and scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.)
- Experience with statistical models (e.g., logistic regression, supervised learning approaches) and a solid foundation in machine learning methods
- Demonstrable, day\-to\-day usage and expert knowledge of AI\-forward coding tools such as Claude and Cursor
- Excellent problem\-solving skills and the ability to navigate highly ambiguous technical and business challenges with sound judgment
- Experience with advanced machine learning frameworks and cloud\-based data science platforms is a plus
Helpful Extras and Unique Skills
- Experience with handling and modeling data in the healthcare industry is a plus
- AWS certifications, like Certified Data Engineer – Associate, strongly preferred
- Masters or PhD degree in computer science, engineering, mathematics, operations research, or in a highly quantitative field
- Experience building generative AI applications on AWS using services such as Amazon Bedrock, Amazon Bedrock AgentCore and Amazon SageMaker
- Experience with design, deployment, and evaluation of Large Language Model (LLM)\-powered agents and tools and orchestration approaches
- Experience with design, development, and optimization of high\-quality prompts and templates that guide the behavior and responses of LLMs
- Experience with open source frameworks for building applications powered by LLMs like Strands, LangChain, LlamaIndex.
- Hands on experience building cloud applications
You'll Do Well Here if You Are
- A doer. You see something broken and fix it. You'd rather move on clarity than wait for certainty.
- A fast learner who knows you don't know everything. The AI landscape changes weekly. You're senior enough to know better and curious enough to keep learning anyway.
- Direct in a way that makes the work better. You give honest feedback. You'd rather have the hard conversation than blow smoke.
- Obsessed with craft. You know genius is in the details. You ship exceptional, not perfect, and you don't put your name on work you wouldn't stand behind.
- Built for ownership. You honor commitments, admit mistakes fast, and back your teammates when a decision costs something. No handoffs, no finger\-pointing.
- All in. You treat clients' businesses like your own. You take the work seriously without taking yourself seriously.
- Resourceful when the budget, timeline, or team is tight. Constraints don't slow you down. They sharpen you.
- Glad to be in the room with people who care as much as you do. Our teams average fifteen\-plus years of experience. We hire people who push each other to do better work.
*An offer of employment may be conditional upon successful completion of a background check in accordance with local legislation and our candidate privacy notice. Your current employer will not be contacted without your permission. We are committed to ensuring equal employment opportunities for all job applicants and employees. Employment decisions are based upon job\-related reasons regardless of an applicant's race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, marital status, genetic information, protected veteran status, or any other status protected by law.*
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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Robots & Pencils, 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 $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Robots & Pencils AI Hiring
Robots & Pencils has 4 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Seattle, WA, US, Remote, US. Compensation range: $209K - $230K.
Location Context
AI roles in Seattle pay a median of $228,700 across 516 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.
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