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About This Role
### About the Role
We're a small, fast\-moving AI productivity startup (\~25 people) building an autonomous AI executive assistant that operates across email, calendars, meetings, and business software. This role owns the measurement system that determines whether our AI agent is genuinely improving in ambiguous, real\-world environments.
You'll partner closely with AI Agent Capabilities engineers to produce the evidence that drives product decisions, model choices, and release quality — turning hard questions about agent behavior into rigorous, actionable answers.
Visa sponsorship is not available for this role.
### What You'll Do
- Architect and maintain automated evaluation pipelines that measure agent quality across product surfaces.
- Translate product capabilities into explicit pass, partial\-pass, and failure criteria for complex multi\-step tasks.
- Build representative gold datasets and regression suites covering real workflows, edge cases, and adversarial scenarios.
- Define and track metrics including task success, tool\-selection accuracy, instruction adherence, factual consistency, latency, cost, and reliability.
- Design deterministic and model\-based graders, calibrate LLM\-as\-a\-judge systems, and monitor grader agreement.
- Compare models, prompts, and implementations using rigorous offline experiments and production evidence.
- Analyze traces and production outcomes to identify root causes and build a practical failure taxonomy.
- Convert production failures into regression cases and continuously close gaps in evaluation coverage.
- Build dashboards and release\-quality signals that make results actionable for engineering, product, and leadership.
- Partner with capability engineers to recommend improvements and verify that fixes raise quality without introducing unacceptable regressions.
### What We're Looking For
Required experience (dealbreakers):
- 4\+ years in Applied Data Science or Machine Learning roles, with a focus on building and delivering evaluation systems, automated data pipelines, or production ML infrastructure.
- Demonstrated experience designing and implementing automated evaluation frameworks, success criteria, and regression suites for complex AI/ML or agentic systems.
- Production\-grade proficiency in Python and SQL, with hands\-on experience building and maintaining automated analytical pipelines on large datasets.
Core requirements:
- Experience applying statistical and experimental methods — significance testing, variance analysis, sampling — to evaluate non\-deterministic AI/ML systems.
- Experience developing labeled datasets, annotation guidelines, and quality\-control processes for ground\-truth data in dynamic product environments.
- Deep understanding of LLM agent behaviors including tool use, multi\-step execution, retrieval, and practical failure modes.
- Ability to analyze model traces, tool calls, and outputs to identify root causes of failures across model, prompt, tool, and data layers.
- Experience using production telemetry and observability data to monitor system quality, build dashboards, and analyze real\-world user outcomes.
Nice to have:
- Prior hands\-on experience with LLM\-as\-a\-judge systems, model\-based grading, or AI benchmarking platforms.
- Experience shipping or operating production ML products, agentic systems, or customer\-facing consumer software.
- Experience reviewing and adapting public research benchmarks or academic evaluation methodologies to real\-world product problems.
Key traits we value: strong product orientation (prioritizing metrics tied to real user outcomes over convenient proxies), high ownership, analytical rigor, and comfort driving ambiguous quality questions from design through to product decisions.
### Location
This role is based in Palo Alto, CA. Visa sponsorship is not available.
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 CLERA, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
CLERA AI Hiring
CLERA has 14 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, Research Engineer, LLM Engineer. Positions span Palo Alto, CA, US, San Francisco, CA, US, New York, NY, US. Compensation range: $150K - $250K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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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