Interested in this Data Scientist role at Accelint?
Apply Now →Skills & Technologies
About This Role
*Note: This position is contingent upon contract award.*
Accelint is a mission\-driven technology company focused on strengthening national security and supporting critical industries. We build the technologies that help operators and organizations see what’s happening, make faster and better decisions, take action with confidence, and stay ready for what comes next. Our work includes advanced sensors, autonomous systems, mission command and control software, AI\-enabled training and simulation, and tools that improve logistics, maintenance, and overall readiness.
For nearly 30 years we have supported military and civilian agencies across the Department of Defense, U.S. allies and partners, and essential industries. When you join Accelint, your work directly contributes to protecting national security and strengthening the systems our society depends on.
Accelint brings together the talents of Hypergiant, Forward Slope, Systems Innovation Engineering, SoarTech, and Highbury Defense Group under one mission, with each entity contributing deep expertise in its domain. This role is part of our Systems Innovation Engineering (SIE) division, which specializes in readiness and logistics, autonomous platforms, and sensor technologies. SIE develops resilient, integrated systems that enhance situational awareness, sustain mission readiness, and deliver reliable performance across air, ground, and maritime environments.
Job Description
- Senior Data Scientist
- Full Time
- Onsite/Hybrid Preferred
The Senior Data Scientist is responsible for designing, developing, testing, and maintaining software applications to provide advanced analytics for software and edge solutions team. This role focuses on developing innovative solutions driven by exploratory data analysis from complex and high\-dimensional datasets. The Data Scientist will apply knowledge of statistics, machine learning, programming, and data modeling. They use a flexible, analytical approach to design, develop, and evaluate predictive models and advanced algorithms to mine the stores of big data. They generate and test hypotheses and analyze and interpret the results of product experiments. Create data visualizations, dashboards, and reports to communicate findings. This role will involve applying AI/ML techniques to read and process technical data to include CAD models for ingestion into enterprise systems. The Senior Data Scientist will work with product engineers to translate prototypes into new products, services, and features and provide guidelines for large\-scale implementation. This role will require collaboration with other data scientists and software engineers to implement algorithms and provide insights to end users. The ideal candidate has at least 5 years of experience in data science, with a demonstrated ability to collaborate with cross\-functional teams.
Duties \& Responsibilities
- Lead the design, development, and implementation of statistical techniques and algorithms.
- Write clean, maintainable, and efficient code, and ensure best practices in coding standards.
- Test algorithms against key benchmarks and implement techniques to enhance performance.
- Work with developers, solution architects, and DevSecOps engineers to incorporate algorithms within software architectures and effectively deploy the algorithms within existing code base.
- Work with engineers to incorporate prototype algorithms into new products, services, and features and provide guidelines for large\-scale implementation.
- Research and identify areas for applications of AI/ML in support of the program.
- Work with the development team and government teams to align AI/ML efforts.
- Prepare and maintain comprehensive technical documentation related to algorithm development.
- Ensure accuracy and completeness of all documentation.
- Mentor junior data scientist and coordinate activities to complete key data science tasks
- Foster effective collaboration with cross\-functional teams to achieve project objectives.
- Communicate complex technical information clearly and effectively.
- Utilize advanced software development tools and methodologies to support project requirements.
- Integrate software development tools and methodologies into the workflow to improve efficiency and accuracy.
- Performs other duties as assigned.
Required Qualifications
- Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or related quantitative field.
- At least 5 plus years of experience in data science or related discipline.
- Experience analyzing system performance data and time series data.
- Proficiency in Python and its data science libraries (Pandas, NumPy, Scikit\-learn, etc.).
- Strong background in statistical analysis, forecasting techniques, and anomaly detection
- Experience with SQL and relational databases.
- Knowledge of data visualization tools for creating operational dashboards (Tableau, Power BI, Grafana, or similar).
- Understanding of machine learning fundamentals with a willingness to expand AI/ML implementation skills.
- Excellent problem\-solving and analytical skills.
- Superior written and verbal communication skills.
- Currently holds an active U.S. national security clearance or be able to receive and maintain one.
Preferred Qualifications
- Experience working with CAD models and other technical data sets
- Experience researching, developing, and implementing machine learning algorithms and models for tasks such as classification, regression, clustering, anomaly detection, and recommendation systems
- Familiarity with stream processing of real\-time data
- Knowledge of cloud platforms (AWS, Azure, or GCP) and their monitoring services
- Experience with ML frameworks (TensorFlow, PyTorch, or similar)
- Familiarity with version control systems (Git)
- Experience with containerization and orchestration tools (Docker, Kubernetes)
- Exposure to data streaming applications (Confluent, Kafka, RabbitMQ, or similar)
- In\-depth understanding of the aerospace and defense industry.
Physical Requirements
- Prolonged periods sitting at a desk and working on a computer.
- Must be able to lift up to 15 pounds at times.
- This position is hybrid located in St. Paul, MN.
- Fully remote candidates may be considered with increased expectation for travel.
- This position may require the ability to travel up to 10%.
Work Environment
Hybrid work arrangements are strongly preferred for this role to support regular in\-person collaboration with program and project teams. Fully remote candidates may still be considered; however, remote employees should expect increased travel requirements to support onsite team engagement. Employees may telework on designated days or as needed with supervisor approval, provided they maintain established communication expectations and effectively meet job responsibilities. Travel may be required 1–2 times per month to customer sites, subcontractor locations, and other company offices.
Clearance Requirements
Some positions will require access to U.S. National Security information. Positions that require this access will be required to receive and maintain a U.S. government personnel security clearance (PCL). In order to qualify for this position, the candidate must be a US Citizen and either currently possess this National Security eligibility or be able to complete the investigation application process with a favorable determination and maintain that eligibility throughout their employment.
Pay Scales \& Benefits
The listed pay scale reflects the broad, minimum to maximum, pay scale for this position for the location for which it has been posted and is not a guarantee of compensation or salary. Other compensation considerations may include, but are not limited to, job responsibilities, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, or other applicable factors.
Benefits include…
Paid Time Off
Paid Company Holidays
Medical, Dental \& Vision Insurance
Optional HSA and FSA
Base and Voluntary Life Insurance
Short Term \& Long\-Term Disability Insurance
401k Matching
Employee Assistance Program
The pay range for this role is:
128,030 \- 153,613 USD per year(St. Paul, MN)
Salary Context
This $128K-$153K range is below the median for Data Scientist roles in our dataset (median: $162K across 211 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 3,824 AI roles we're tracking, Data Scientist positions make up 7% of the market. At Accelint, 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 $200,000 based on 697 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($140K) sits 30% below the category median. Disclosed range: $128K to $153K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Accelint AI Hiring
Accelint has 2 open AI roles right now. They're hiring across Data Scientist. Based in Saint Paul, MN, US. Compensation range: $126K - $153K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.