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About This Role
Valence has built the only AI native coaching platform for enterprise, offering personalized, expert, and human\-like guidance and support to any leader or employee. We’re not just talking about the future of work — we’re building it now, with the most innovative Fortune 500 companies across healthcare, financial services, manufacturing, and technology.
Learn more about our work with Microsoft: Valence Brings AI Coaching More Deeply Into the Flow of Work with Microsoft 365
Our focus is on the problems that actually decide whether AI changes how organizations operate — the ones with no playbook, no obvious answers, and no guarantee of success. If you want to be part of the small group that defines how AI transforms the future at a global scale, this is your chance.
And this isn’t for everyone. We’re not looking for people who want predictability or incremental progress. We only want those who are restless at the edge of what’s possible, who get bored when things feel “done,” and who are driven to redefine what AI can mean for leaders, companies, and the world. Because at Valence, the work worth doing is the kind that redefines work itself.
The Role
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In this role you’ll be critical in shaping how data informs decisions across our product, customer, and go\-to\-market teams. You’ll uncover user behavior patterns, operational performance insights, and commercial opportunities — all while helping to lay the foundation for a scalable, insight\-driven data culture. You'll collaborate deeply with leaders across Product, Engineering, Sales, and Customer Success to ensure we’re making the smartest possible decisions from day one.
About Valence
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We're the only company pioneering leadership coaching for large enterprises in an AI\-first way. Our mission is to transform how the world's biggest companies approach learning and development, helping teams work better together through AI\-powered personalization that adapts to individual goals and organizational culture using the latest advances in machine learning and natural language processing.
We've been featured in Harvard Business Review, TIME, World Economic Forum, Financial Times, Forbes and an Inc. 5000 fastest\-growing private companies in America. Our clients represent the most diverse and sophisticated enterprise AI implementations globally, including Coca\-Cola, Delta, Nestlé, General Mills, Schneider Electric, Deutsche Telekom, AstraZeneca, Prudential, CVS and Bristol Myers Squibb.
Working at Valence means you'll work directly with Fortune 500 technology leaders, building expertise through the most complex enterprise deployments while gaining insight into diverse organizational approaches to AI transformation. These aren't just any enterprise clients \- they're the companies defining what AI\-first business transformation looks like across every major industry.
What You'll Do
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- Product \& Customer Analytics
- You will analyze platform usage, feature engagement, and user interaction with our AI\-powered coaching tools to surface actionable insights.
- You’ll identify friction points and areas of opportunity across key user journeys.
- You’ll support rigorous A/B testing, evaluate AI model performance, and help assess the impact of new features on user experience and outcomes.
- Reporting \& Visualization
- You’ll develop self\-serve dashboards and recurring reporting frameworks to democratize access to data across teams.
- You will translate complex data into clear, compelling stories for technical and non\-technical audiences to support data\-informed decision\-making.
- Data Infrastructure \& Collaboration
- You’ll collaborate with Product and Engineering teams to define tracking requirements and ensure accurate, consistent data collection.
- You’ll identify and recommend improvements to data processes that enhance quality, efficiency, and trust in analytics across the organization.
What We’re Looking For
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- You have 5\+ years of experience in a data analytics role, ideally within a B2B SaaS or AI\-focused startup environment.
- You hold at least a Bachelor’s degree in Data Science, Statistics, Computer Science, Economics, or a related quantitative field.
- You are highly proficient in SQL and comfortable writing complex queries and working with diverse database structures.
- You have hands\-on experience with Python (using libraries like Pandas, NumPy, Matplotlib, or Seaborn) or R for data analysis and visualization.
- You’re familiar with BI tools such as Tableau, Looker, Power BI, or equivalent platforms.
- You’re familiar with using Excel or Google Sheets for ad hoc analysis and data exploration.
- You may also bring experience with product analytics platforms (e.g., Mixpanel, Amplitude), cloud environments (e.g., AWS or GCP), or data warehousing concepts.
- You know how to break down complex business problems into structured analyses.
- You are skilled at identifying trends, patterns, and outliers in data to uncover key insights.
- You can clearly communicate findings—both in writing and verbally—to technical and non\-technical stakeholders alike.
- Nice to have: You’ve worked in a fast\-paced, early\-stage startup, you bring familiarity with AI/ML concepts, you understand SaaS metrics and enterprise customers.
What you'll get
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Ownership \& Rapid Growth
- Outsized missions from day one, with direct responsibility for company\-defining projects
- Work alongside the executive team with transparency into strategy and decision\-making
- Influence on direction through real\-time customer feedback and market insights
AI\-First Operator
- Work directly with cutting\-edge AI models and next\-generation platforms
- Build expertise in enterprise AI implementation across Fortune 500 companies and multiple industries
- Establish yourself as a recognized leader among peers in shaping how AI transforms work at a global scale
Compensation
- Competitive salary including base \+ bonuses
- Comprehensive health coverage (medical, dental, vision) from day one
- Generous PTO, company\-wide R\&R shutdowns, and paid parental leave
- Retirement plan support for US and global employees
Equity
- Meaningful ownership in a venture\-backed company at a growth inflection point
- Financial upside that comes from scaling fast
- Top\-up grants as we scale and you deliver exceptional performance — your compensation grows alongside your impact
Top\-Performing Culture
- A culture built for top talent: intensity to win, growth without limits, and a team that solves hard problems and celebrates big wins together
Learn more about us and meet our team here
Location and Work Environment
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This role is 3 days a week (Tues \- Thurs) minimum in office in Toronto or NYC depending on your residency. Candidates must be comfortable working with colleagues in different time zones (UK), and have valid travel documents without work authorization restrictions.
Diversity and Inclusion
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We are dedicated to creating a diverse and inclusive environment where everyone feels valued and supported. We encourage applications from candidates of all backgrounds and offer accommodations upon request throughout the hiring process. If you have any questions, please reach out to Allison Langille, Head of People, at [email protected].
Employment Verification \& Commitment
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We use third\-party services to verify employment history, education, and other information relevant to your candidacy. Employment is contingent upon the successful completion of these verification checks. This is a full\-time role that requires a high level of focus, availability, and commitment. Employees may not hold concurrent full\-time employment with another organization while employed at Valence. Any outside consulting, advisory, freelance, or other professional work must be disclosed and approved in advance and must not interfere with job responsibilities, availability, performance, or create a conflict of interest \- including risks related to confidentiality, intellectual property, or competition.
*Valence takes candidate fraud seriously. We verify identity, LinkedIn profiles, employment history, and references; any misrepresentation during the hiring process is grounds for withdrawing an offer or ending employment. All offers are contingent on successful completion of these checks.*
\#LI\-HYBRID
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 Valence, 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.
Valence AI Hiring
Valence has 1 open AI role right now. They're hiring across Data Scientist. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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.
Frequently Asked Questions
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