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About This Role
Build, Deploy, and Maintain AI for an Unpredictable World
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Striveworks helps organizations harness the power of artificial intelligence to solve real\-world national security and business challenges by serving as the command center between data, models, and business outcomes. Founded by data scientists and engineers, Striveworks set out to make the journey from deployment to ongoing optimization simple and effective.
With Striveworks, organizations aren’t just deploying AI—they’re building systems that remain reliable, adaptable, and ready to scale in an unpredictable world. Mission\-critical operations require models that perform where they’re deployed, scale as workloads grow, and adapt rapidly as AI capabilities advance. Striveworks meets these demands, increasing reliability and performance while lowering costs—and enabling confident, data\-driven decision\-making in dynamic environments.
The Role
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As a Data Scientist at Striveworks, you’ll be challenged—and trusted—on day one to be a core contributor to the projects, products, and direction of the company. You will represent Striveworks as a technology builder on projects and solutions that leverage Chariot, our proprietary AI operations (AIOps) platform, and you will inform and contribute to future capabilities of that platform. You will work as part of a team of data scientists, machine learning engineers, software engineers, and DevOps engineers to transform machine learning models into functional products.
You’re right for this opportunity if you value and possess technical expertise and enjoy pushing the boundaries of your own capabilities. You’re outcome driven and are passionate about applying both software and data science to solve real\-world problems. You know that being customer focused, rigorous in approach, clear in communication, and able to identify repeatable value opportunities are all critical to success. You are able to sense the needs of the customer, identify evolving demands, and then synthesize that feedback into actionable suggestions for Striveworks’ product teams.
Your day\-to\-day will include:
- Developing and validating machine learning models and custom analytic algorithms that are applied to image, video, text, geospatial, time series, and structured data
- Implementing AI\-based software solutions for cloud and edge environments
- Conducting mission\-critical fieldwork in support of customers and other stakeholders
This position offers a fully remote work environment, or you can work hybrid/on site at customer locations at Joint Base Lewis–McChord in Tacoma, WA. If remote, you will be expected to travel up to 30% of the time. If local, you will be expected to travel up to 25% of the time.
The Right Fit
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In addition to the specific skills and expertise detailed below, we are looking for individuals who share our values. Sharing a set of values allows us to move at the speed of trust.
Collectively, we value a high\-trust work environment where people respect each other and use candor kindly and constructively. We value work that intersects passion and perseverance, we geek out about the potential of our contributions, and we find joy in working hard on things that matter. Finally, we value taking ownership, having agency, and feeling individual responsibility for collective results.
Here’s what we’re looking for:
- BS degree in computer science, machine learning, mathematics, or a related discipline and 2\+ years of relevant experience
- Proficiency in machine learning and data science and in applying both to image and video data
- Proficiency in implementing and analyzing data structures and algorithms
- Proficiency in programming languages and libraries common to machine learning; excellence in Python is essential, as is knowledge of libraries like TensorFlow, PyTorch, and/or scikit\-learn
- Exposure to software development life cycle and tools (e.g., Git, Agile)
- Active Secret (or above) US security clearance
- Due to the nature of this role, candidates must have US citizenship
The Wish List
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We’re very interested in candidates who possess the above qualifications, and we appreciate and consider the addition of:
- An advanced degree (e.g., MS, MEng, PhD) in computer science, machine learning, mathematics, or a related discipline
- Experience deploying machine learning and data science, and applying both to production environments
- Exposure to DevOps and Cloud infrastructure (e.g., Docker, Kubernetes, CI/CD, major cloud providers)
- Experience processing a variety of unstructured data types (e.g., imagery, full\-motion video, text, acoustic, sonar, RF, geospatial, graphs, telemetry signals)
- Experience building AI agents and agentic workflows
- Experience implementing ETL pipelines, data pipelines, and/or workflow automation
- Experience developing software in a compiled programming language (e.g., Go, Rust, C\+\+, Java)
- Experience building full\-stack applications (i.e., back end, front end, REST)
- Experience delivering technology solutions in secure government environments
- Experience working with federal, state, and/or local government customers
The anticipated base pay range for this position is $160,000–$190,000/year. Striveworks’ total compensation package includes a competitive base salary, equity grants, and cash bonuses.
The Benefits
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- Medical/dental/vision insurance
- Voluntary life, long\-term disability, accident, and hospital indemnity insurance
- HSA and FSA (including dependent care FSA) plans
- 401(k) plan
- Unlimited PTO
- Paid parental leave
Check us out on Built In!
*Striveworks is an Equal Opportunity Employer and does not discriminate in employment on the basis of race, color, religion, belief, sex (including pregnancy and gender identity or expression), national origin, social or ethnic origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service, or other non\-merit factors. Striveworks will not tolerate discrimination or harassment of any kind.*
*If you require assistance or a reasonable accommodation in the application process, please contact Operations at* *[email protected]**.*
*In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete an employment eligibility verification form upon hire.*
*Striveworks is a participating employer in the E\-Verify program.*
Salary Context
This $160K-$190K range is above the median for Data Scientist roles in our dataset (median: $160K across 245 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 4,133 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Striveworks, 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 $198,000 based on 868 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($175K) sits 12% below the category median. Disclosed range: $160K to $190K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Striveworks AI Hiring
Striveworks has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Remote, US. Compensation range: $190K - $190K.
Remote Work Context
Remote AI roles pay a median of $173,300 across 2,012 positions. About 14% of all AI roles offer remote work.
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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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