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About This Role
About Us:
As a global manufacturer of complex aircraft engine components, Pursuit Aerospace is founded on a commitment to relentless, continuous, operational improvement and extraordinary customer service. We pride ourselves on competitive cost structure, exceptional on-time delivery, and industry-leading quality.
Pursuit Aerospace cultivates long-term relationships with our customers around the world through respect, teamwork, technology, and trust. We are driven to develop industry leading process innovations and manufacturing techniques on behalf of our customers.
About the Opportunity:
The senior data scientist will build data, analytics, and AI solutions to support SIOP (Sales, Inventory, Operations Planning) transformation and delivery business impact on inventory and customer service. The senior data scientist will function like a forward-deployed engineer who takes a supply chain problem from ambiguity to financial impact: framing the problem, shaping the data and models, building and iterating fast, and owing the solution end to end. This role will work shoulder-to-shoulder with the business, move at its pace, and turn large, messy operational data into robust models, dashboards, and AI-powered tools that change decisions and outcomes.
Location:
Remote in the U.S., and travel 10-20%.
Responsibilities:
Data Science & Machine Learning
- Develop and produce machine learning, statistical, forecasting, and time-series models that solve complex operational and business problems.
- Build solutions supporting key business areas including inventory optimization, demand forecasting, clear-to-build analytics, on-time delivery, production planning, operational efficiency, and cost reduction.
- Perform exploratory analysis, feature engineering, model selection, validation, optimization, deployment, and ongoing performance monitoring.
- Develop predictive and prescriptive analytical solutions that translate large and complex datasets into measurable business outcomes.
- Develop AI-powered applications and tools, including Generative AI and Large Language Model solutions where they provide measurable business value.
End-to-End Data Science Products
- Independently build end-to-end data science products, from business problem definition and data preparation through modeling, application development, deployment, and adoption.
- Develop robust analytical datasets and transformation workflows using Python and SQL to support machine learning and advanced analytics.
- Work with large, complex datasets across multiple enterprise systems and independently identify, clean, transform, and integrate the data required for analytical solutions.
- Develop dashboards, analytical applications, APIs, or decision-support tools that enable business users to consume model outputs and analytical insights.
- Build production-quality solutions with appropriate testing, version control, monitoring, documentation, and maintainability.
Data & Technology Collaboration
- Leverage modern cloud and data technologies such as AWS, Snowflake, Spark, S3, and related services to develop scalable analytical and machine learning solutions.
- Understand modern data architecture concepts including data lakes, ETL/ELT, streaming data, APIs, and cloud-native data platforms sufficiently to design data science solutions that integrate effectively with enterprise platforms.
- Collaborate with Product, Engineering, Operations, Supply Chain, and business stakeholders to translate business capabilities and requirements into practical analytical solutions.
- Deliver high-impact analytics: partner with cross-functional leaders to scope the problems and ship solutions that move real metrics: inventory, on-time delivery, and cost.
- Build end-to-end products: build products independently and iterate quickly based on business feedback including data pipeline and snowflake tables, forecasting and time-series models, inventory and clear-to-build analytics, dashboards, and AI-powered tools.
- Run analytics assets in production: keep all assets accurate and reliable and manage analytics operations leveraging technology
- Engineer trustworthy data: turn large, messy operational data into well-structured, trusted datasets, and develop continuous improvement framework to enhance data quality and integrity
- Drive decisions and adoption: translate analytical results and tools into clear, decision-ready recommendations and drive adoption across the business
Required Qualifications:
- Bachelor’s degree
- 4 + years in a data science role demonstrating solving complex business problems and independently building robust solutions that improve outcomes
- 4 + years of hands-on experience using SQL and Python for data analysis, data transformation, and analytical solution development.
- 4 + years of experience developing machine learning, demand forecasting, or time-series models.
- Must be authorized to work in the U.S. on a full-time basis without sponsorship now or in the future. The Company cannot offer employment to visa holders who require employer sponsorship in the future or cannot work now on a full-time basis.
- Must be able to perform work subject to ITAR/EAR regulations.
Preferred Qualifications:
- Bachelor’s degree in data science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
- Master’s degree in a quantitative or technical field.
- Proficient in SQL, Python, ML modeling, and time-series analytics; hands-on with Codex for AI-assisted coding against Snowflake tables, pipeline orchestration, GitHub for CI/CD, Azure data platform
- Demonstrated experience independently developing and deploying machine learning, statistical, or time-series forecasting models, including end-to-end ownership from problem formulation and data preparation through model validation, deployment, and monitoring. Experience
- Experience independently working with large, complex, and imperfect datasets, including data exploration, transformation, feature engineering, data quality remediation, and development of production-ready analytical datasets.
- Experience designing and implementing data models and ETL/ELT pipelines in modern cloud data environments such as Snowflake, AWS, Azure, or GCP.
- Experience developing production-quality analytical or machine learning solutions using testing, version control, CI/CD, monitoring, and documentation.
- Experience with AI-assisted product development and using AI tools to plan, build, quality-check, debug, and improve code.
- Supply chain analytics knowledge such as demand forecasting, demand sensing, inventory management and clear-to-build reporting
- Aerospace and/or manufacturing experience
- Familiarity with ERP systems and hands-on experience with purchase order, production order, and inventory data
Working Conditions:
- Requires mobility in a manufacturing plant environment while using Personal Protective Equipment.
- Must be able to frequently sit, stand and walk.
- Must be able to lift and carry up to 15 pounds.
- Must be able to have prolonged periods sitting at a desk and working on a computer.Must be able to travel between locations or to suppliers up to 25% of time.
Acknowledgements:
The above job description is not intended to be an all-inclusive list of duties and standards of the position. Incumbents will follow any other instructions, and perform any other related duties, as assigned by their supervisor.
Benefits:
Pursuit Aerospace also offers a variety of benefits, including health and disability insurance, 401(k) match, flexible spending accounts, EAP, paid time off, and company-paid holidays. The specific programs and options available to an employee may vary depending on date of hire, schedule type, and the applicability of collective bargaining agreements, among other things.
Equal Opportunity Employer:
Pursuit Aerospace is an Equal Opportunity Employer. We adhere to all applicable federal, state, and local laws governing nondiscrimination in employment. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Salary Context
This $116K-$174K range is below the median for Data Scientist roles in our dataset (median: $161K across 138 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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Pursuit Aerospace, 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 1,503 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($145K) sits 24% below the category median. Disclosed range: $116K to $174K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Pursuit Aerospace AI Hiring
Pursuit Aerospace has 1 open AI role right now. They're hiring across Data Scientist. Based in US. Compensation range: $174K - $174K.
Location Context
AI roles in Austin pay a median of $209,100 across 306 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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