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About This Role
Data Scientist
ROLE
We need an experienced Data Scientist at the Office of the Under Secretary of the Army for Personnel and Readiness (OUSW(P\&R)). The office oversees manpower and personnel data systems in support of Army\-wide workforce planning and readiness initiatives. In this role, you will develop and automate Python scripts for data collection, cleaning, and preprocessing; build and implement machine learning models and predictive analytics; maintain and update ETL pipelines for manpower and personnel data sources; create data products and Qlik/Tableau reports and dashboards; and produce documentation covering report usage, data refresh procedures, and dashboard architecture. This is a full\-time opportunity. We can offer a competitive salary and a comprehensive benefits package.
Apply today!
RESPONSIBILITIES* Develop and automate Python scripts for collecting, cleaning and preprocessing data.
- Work with cross functional teams to translate business requirements into data\-driven solutions.
- Create reports and visualizations in Qlik sense to communicate findings and insights effectively, matching needs of stakeholders and users.
- Build and implement machine learning models and/or predictive analytics.
- Maintenance of Historical Data for Faces to Spaces Data Model
+ Troubleshooting issues with ETL to a quick resolution
+ Updating ETL to reflect new data elements for new and existing manpower and personnel data sources
+ Data ingestion and transformation
+ Monthly updates / automation of new data ingestion and visualization
- Create Data Products
+ Interpret data requirements and develop Qlik/Tableau Reports
+ Refactoring/repointing data ingest of dashboards from databases
+ Create documentation on how to use reports, how to refresh the data and overall dashboard architecture
REQUIRED EDUCATION / CERTIFICATIONS* Bachelor’s degree in information technology, computer science, mathematics, or a related field \- required
REQUIRED EXPERIENCE / SKILLS* 3\-10 years’ experience directly related to the outlined role responsibilities
- 3 years’ experience as a Data Analyst or Data Scientist, with demonstrated hands\-on experience with technologies such as Databricks, Python, Spark, and Pandas
- Proficiency in Qlik
- Experience with building, training and deploying machine learning
- Detail\-oriented mindset with a commitment to delivering high\-quality results with little direction/oversight
- Comfortable using AI tools to improve product quality and delivery speed
PREFERRED EXPERIENCE / SKILLS* Experience with Microsoft Power BI
- Experience with cloud data structures such as AWS S3, delta and external tables
- Experience with Machine Learning models and/or Generative AI
- Demonstrated experience building end\-to\-end data pipelines in Databricks, with a proven ability to integrate existing legacy systems
LOCATION* Remote – Eastern Standard Time zone – preferred but not required
CLEARANCE* U.S. Citizenship \- required
- Active Secret clearance \- required
CLIENT* Office of the Under Secretary of War for Personnel and Readiness (OUSW(P\&R))
TRAVEL* No travel required
WORK HOURS* 40 hours per week
- 8 hours per working day
EMPLOYMENT CLASSIFICATION* Employment Classification Eligibility — W2
COMPENSATION* Salary range: $135,000 – $150,000
- Benefits: Benefits package includes options for health, dental, and vision insurance coverage; 401k contribution options
*West 4**th* *Strategy is an Equal Opportunity (EEO) employer. All qualified applicants will receive consideration for employment without regard to* *race, color, creed, religion, gender, sexual orientation, ancestry, national origin, age, marital status, mental disability, physical disability, medical condition, pregnancy, political affiliation, military or veteran status, or any other basis prohibited by federal or state law.*
Other Considerations: *applicants will be subject to a background investigation. Individual’s primary workstation is located in an office area. The noise level in this environment is low to moderate. Regularly required to sit for extended periods up to 80% of the time; frequently required to move about to access file cabinets and use office equipment such as PC, copier, fax, telephone, cell phone, etc. Occasionally required to reach overhead, bend, and lift objects of up to 10 lbs. Specific vision abilities required by this job include the use of computer monitor screens up to 80% of the time.*
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Salary Context
This $135K-$150K range is below the median for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At West 4th Strategy, 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. This role's midpoint ($142K) sits 26% below the category median. Disclosed range: $135K to $150K.
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.
West 4th Strategy AI Hiring
West 4th Strategy has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $150K - $150K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 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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