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About This Role
Altamira Technologies has a long and successful history providing innovative solutions throughout the U.S. National Security community. Headquartered in McLean, Virginia, Altamira serves the defense, intelligence and homeland security communities worldwide by focusing on creating innovative solutions leveraging common standards in architecture, data and security. Altamira believes that our people and the culture of our company differentiate us from other companies.
Position: Junior Data Scientist
Position Location: Fort Bragg, North Carolina
Position Description:
A data scientist will have skills sets of both data analysts and data engineers. Data scientists are responsible for designing, implementing, and maintaining a data pipeline. In addition, data scientists shall interpret and analyze complex sets of data, as well as plan, execute, and manage ML projects with cloud\-native platforms and advanced ML solutions. They understand some of the most challenging processes, technologies, and can leverage a vast array of methodologies in the field, such as data mining, natural language programming, and machine learning.
Data scientists must have a combination of skills that include programming, mathematical modeling, statistics, and domain knowledge. They must combine an advanced math and statistics background with programming, domain knowledge, and communication skills to analyze data, create applied mathematical models, and present results in a form useful to the organization. They must also be able to understand and manipulate structured and unstructured large data sets, which requires proficiency in distributed SQL programming, relational and non\-relational data queries, general programming languages (such as Python and R,) and machine learning techniques.
Experience:
- Interpret and analyze data using exploratory mathematicand statistical techniques based on the scientific method.
- Coordinate research and analytic activities utilizing various data points (unstructured and structured) and employ programming to clean, massage, and organize the data
- Experiment against data points, provide information based on experiment results and provide previously undiscovered solutions to command data challenges.
- Coordinate with Data Engineers to build Data environments providing data identified by other data professionals
- Apply and develop scientific methodology, statistics, and algorithms to discover and frame relevant problems, hypotheses, and opportunities.
- Develop predictive and prescriptive modeling, natural language processing (NLP), Robotic Process Automation (RPA), text mining and processing, clustering, forecasting methods, and other advanced statistical techniques.
- Design and automate processes to facilitate the manipulation and analysis of data. Manage and integrate data across dissimilar data sets. Analyze large\-scale structured and unstructured data.
- Use frameworks such as Spark and Hadoop to conduct large\-scale data processing. Perform statistical modeling and create data visualizations using products like Tableau, Microsoft Power BI and R Shiny.
- Research, design, and implement algorithms to solve complex problems. Program using R, Python (NumPy, SciPy, Pandas) or similar analytical languages.
- Perform data engineering, data processing and modeling techniques using cloud\-based data management, data science, and ML platforms such as Databricks, IBM Cloud Pak, Cloudera, and Snowflake.
- Communicate complex concepts and hypothesis to a non\-technical audience through digital storytelling.
Education:
- Bachelor’s degree in a STEM field is required. Master’s degree is preferred in Operations Research, Industrial Engineering, Applied Mathematics, Statistics, Physics, Computer Science, or related fields.
- Bachelor’s degree is acceptable in the above fields if the incumbent has training and verifiable work experience in a related field.
- Proficient with one or more programming languages (Java, C\+\+, Python, R, etc.).
- Proficient in Agile Development and Git Operations.
- Demonstrated experience applying data science methods to real\-world data problems.
- TS/SCI clearance is preferred
- Minimum SECRET Clearance to start with the willingness and ability to obtain TS/SCI.
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 Altamira Technologies Corp., 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. Entry-level AI roles across all categories have a median of $110,000.
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.
Altamira Technologies Corp. AI Hiring
Altamira Technologies Corp. has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Fairborn, OH, US, Fort Bragg, NC, US.
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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