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About This Role
Job Description
CLEARANCE REQUIRED: TS/SCI w/ Polygraph
Other Requirements: U.S. Citizenship
We are sourcing a Data Scientist to join the team and spearhead this effort. The Data Scientist will be responsible for:
Characterizing and understanding the entity data
Determining if data standardization or normalization is required
Evaluating various AI/ML approaches to performing entity resolution
Determining the appropriate tools and models to leverage
Designing and prototyping the end-to-end solution
Evaluating the prototype output and performance
Help with the eventual productization of the entity resolution solution
The ideal candidate will have experience with:
Developing software using Java, Python, GoLang or similar languages
Working with and evaluating large data sets
Developing data ingest and processing pipelines
Working with and evaluating embedder/encoder models
Developing feature vectors and working with vector databases such as Qdrant
Working with software development teamsBachelor's degree or higher from an accredited college or university in a quantitative discipline (e.g., statistics, mathematics, operations research, engineering or computer science). A minimum of ten (10) years of experience in two (2) or more of the following: designing/implementing machine learning, data mining, advanced analytical algorithms, advanced statistical analysis, artificial intelligence, or software engineering with data analysis software such as R, Python, SAS, or MATLAB. An additional four (4) years of experience in software development, cloud development, analyzing datasets, or developing descriptive, predictive, and prescriptive analytics can be substituted for a Bachelor's degree.
A Master’s Degree from an accredited college or university in a quantitative discipline can be substituted for two (2) years of experience for a total of eight (8) years of experience required. A Doctoral Degree from an accredited college or university in a quantitative discipline can be substituted for four (4) years of experience for a total of six (6) years of experience required.
Produce data visualizations that provide insight into dataset structure and meaning
Work with subject matters experts (SMEs) to identify important information in raw data and develop scripts that extract this information from a variety of data formats (e.g., SQL tables, structured metadata, network logs)
Incorporate SME input into feature vectors suitable for analytic development and testing
Translate customer qualitative analysis process and goals into quantitative formulations that are coded into software prototypes
Develop and implement statistical, machine learning, and heuristic techniques to create descriptive, predictive, and prescriptive analytics
Develop statistical tests to make data-driven recommendations and decisions
Develop experiments to collect data or models to simulate data when required data are unavailable
Develop feature vectors for input into machine learning algorithms
Identify the most appropriate algorithm for a given dataset and tune input and model parameters
Evaluate and validate the performance of analytics using standard techniques and metrics (e.g. cross validation, ROC curves, confusion matrices)
Oversee the development of individual analytic efforts and guide team in analytic development process
Guide analytic development toward solutions that can scale to large datasets
Partner with software engineers and cloud developers to develop production analytics
Develop and train machine learning systems based on statistical analysis of data characteristics to support mission automation
Lead a team of data scientists in the development of multiple analytic efforts
Work with customers and SMEs to define analytic requirements and guide the team in formulating analytics that meet requirements
Guide the transition of prototyped analytics to production system
Understand emerging machine learning and pattern recognition algorithms and guide a team of data scientists in integrating state-of-the-art algorithms into solutions
Delegate analysis responsibilities to one or more team members and monitor performance
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Required Skills
The ideal candidate will have experience with:
Developing software using Java, Python, GoLang or similar languages
Working with and evaluating large data sets
Developing data ingest and processing pipelines
Working with and evaluating embedder/encoder models
Developing feature vectors and working with vector databases such as Qdrant
Working with software development teamsBachelor's degree or higher from an accredited college or university in a quantitative discipline (e.g., statistics, mathematics, operations research, engineering or computer science). A minimum of ten (10) years of experience in two (2) or more of the following: designing/implementing machine learning, data mining, advanced analytical algorithms, advanced statistical analysis, artificial intelligence, or software engineering with data analysis software such as R, Python, SAS, or MATLAB. An additional four (4) years of experience in software development, cloud development, analyzing datasets, or developing descriptive, predictive, and prescriptive analytics can be substituted for a Bachelor's degree.
A Master’s Degree from an accredited college or university in a quantitative discipline can be substituted for two (2) years of experience for a total of eight (8) years of experience required. A Doctoral Degree from an accredited college or university in a quantitative discipline can be substituted for four (4) years of experience for a total of six (6) years of experience required.
Produce data visualizations that provide insight into dataset structure and meaning
Work with subject matters experts (SMEs) to identify important information in raw data and develop scripts that extract this information from a variety of data formats (e.g., SQL tables, structured metadata, network logs)
Incorporate SME input into feature vectors suitable for analytic development and testing
Translate customer qualitative analysis process and goals into quantitative formulations that are coded into software prototypes
Develop and implement statistical, machine learning, and heuristic techniques to create descriptive, predictive, and prescriptive analytics
Develop statistical tests to make data-driven recommendations and decisions
Develop experiments to collect data or models to simulate data when required data are unavailable
Develop feature vectors for input into machine learning algorithms
Identify the most appropriate algorithm for a given dataset and tune input and model parameters
Evaluate and validate the performance of analytics using standard techniques and metrics (e.g. cross validation, ROC curves, confusion matrices)
Oversee the development of individual analytic efforts and guide team in analytic development process
Guide analytic development toward solutions that can scale to large datasets
Partner with software engineers and cloud developers to develop production analytics
Develop and train machine learning systems based on statistical analysis of data characteristics to support mission automation
Lead a team of data scientists in the development of multiple analytic efforts
Work with customers and SMEs to define analytic requirements and guide the team in formulating analytics that meet requirements
Guide the transition of prototyped analytics to production system
Understand emerging machine learning and pattern recognition algorithms and guide a team of data scientists in integrating state-of-the-art algorithms into solutions
Delegate analysis responsibilities to one or more team members and monitor performance
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About Evlos Technology
Serving our customers, partners, and Solvers, by Advocating for professional and personal improvement and growth, to Solve the most complex problems through evolving technology and ideas.
Evlos Technology was established on the belief that nurturing people and their careers transforms lives and enhances customer satisfaction. Our Solvers are the heartbeat of our company, and their well-being and growth are our top priority. That’s why we cultivate an opportunity-rich culture where our team can excel and reach their full potential. Through our passion to serve the mission, we can achieve great things together.
Our culture is not just shaped by our values, it’s defined by them.
Evlos Technology is looking for problem solvers and “opportunity capitalizers”. These individuals are able to understand how to use their background to solve customer problems and innovate in a manner that brings new opportunities to light.
Why Evlos?
Competitive Pay- We maximize your income potential by passing through as much earned revenue as possible, giving you more control of your hard-earned money.
Referral Bonus- Our unique Referral Bonus Program is the gift that keeps giving! In addition to a referral bonus, you will continue to receive a bonus on each hired referral twice a year for the duration of their tenure with us!
Health Insurance- Healthcare isn’t one-size-fits-all. Evlos provides Solvers with a healthcare subsidy, giving you the flexibility to choose the healthcare option that works best for you and your family.
401k- Regardless of your own contributions, Evlos contributes up to 10% of your income into your retirement account.
Paid Time Off- We believe in a healthy work-life balance. Our standard accrual for each hour you work earns approximately 6 weeks of PTO per year with 40 hours fronted on date of hire. We offer additional leave for maternity leave, paternity leave, military leave, jury duty and bereavement leave.
Company Profit Sharing- Our success is directly tied to your hard work, so we spread the wealth with YOU. Tenure-based profit sharing is paid out annually.
Professional Development- Employees can be reimbursed up to $5,250 per year for pro dev expenses.
Well Tech Benefit- Employees can submit for reimbursement towards wellness and tech related expenses.
Evlos provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Evlos Technology, 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 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Evlos Technology AI Hiring
Evlos Technology has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Annapolis Junction, MD, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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