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About This Role
About Us
Venatôre is a mission\-driven government contractor based in Tampa, FL, supporting federal and civilian agencies with secure cutting\-edge technology, cybersecurity, and mission support services. Our teams are trusted in demanding environments because we integrate with client missions, combining technical depth, operational precision, and innovative thinking to deliver meaningful results where they matter most.
About the Job
The Data Scientist Lead is responsible for leading the organization's data science, data engineering, and analytics functions in support of mission\-critical military operations. This senior technical leadership role oversees the design, development, and operationalization of advanced data analytics, artificial intelligence, and machine learning capabilities that enhance data\-driven decision\-making across the command. Working closely with senior government stakeholders, including the Chief Data Officer (CDO) and Executive Data Officer (XDO), the Data Scientist Lead translates operational requirements into scalable technical solutions while providing strategic direction, technical oversight, and mentorship to a multidisciplinary team. This position requires the ability to manage multiple priorities in a dynamic, mission\-focused environment and may require occasional domestic and international travel.
Responsibilities
Leadership \& Strategic Oversight
- Lead and provide technical oversight for data engineering, data science, analytics, and predictive analytics teams in support of operational mission objectives.
- Establish strategic priorities for data management, advanced analytics, AI/ML, and data engineering initiatives.
- Serve as the senior technical advisor on data science, machine learning, and analytics capabilities.
- Mentor and develop technical staff while promoting best practices, innovation, and continuous improvement.
- Manage multiple concurrent projects, priorities, and deliverables in a fast\-paced operational environment.
Data Science \& Artificial Intelligence
- Lead the development, validation, deployment, and operationalization of advanced statistical models and AI/ML solutions.
- Apply supervised, unsupervised, reinforcement learning, and generative AI techniques, including Large Language Models (LLMs) and multimodal AI, to solve complex mission challenges.
- Guide the full data science lifecycle, including data exploration, feature engineering, model development, validation, deployment, and performance monitoring.
- Evaluate emerging AI and machine learning technologies for mission applicability and operational effectiveness.
Data Engineering \& Architecture
- Oversee the design, implementation, and maintenance of secure, scalable, and fault\-tolerant data pipelines supporting structured and unstructured data.
- Direct ETL/ELT processes, data integration, data warehousing, and modern data lake/lakehouse architectures.
- Ensure data quality, governance, accessibility, and compliance throughout the data lifecycle.
- Support enterprise data management strategies and architecture modernization initiatives.
Stakeholder Engagement \& Mission Support
- Collaborate with the USCENTCOM Chief Data Officer (CDO), Operations Directorate Executive Data Officer (XDO), and other government stakeholders to define operational data requirements.
- Participate in governance boards, technical working groups, and strategic planning forums.
- Translate complex technical findings into clear, actionable recommendations for senior military leadership and non\-technical stakeholders.
- Ensure analytical capabilities directly support operational planning, mission execution, and strategic decision\-making.
Analytics \& Technology
- Guide the use of SQL, Python, R, and other analytical tools to develop data\-driven solutions.
- Oversee implementation and utilization of visualization platforms such as Power BI, Tableau, Qlik, Palantir Foundry, and Maven Smart System (MSS).
- Support enterprise analytics platforms, distributed data processing technologies, and database environments including Apache Spark, Databricks, Hadoop, Oracle, PostgreSQL, MySQL, and Microsoft SQL Server.
- Promote adoption of modern analytics methodologies and industry best practices.
Required Qualifications
- U.S. Citizenship is required.
- Active Top Secret security clearance with SCI eligibility.
- Bachelor's degree in Data Science, Analytics, Computer Science, Information Technology, or a related technical discipline.
- More than 10 years of progressively responsible experience in data science, analytics, or data engineering, including significant leadership experience.
- Demonstrated experience supporting Military, Federal Government, or Intelligence Community organizations.
- Proven experience gathering, implementing, and managing data science requirements for senior government stakeholders.
- Expertise developing and deploying advanced AI/ML solutions using frameworks such as TensorFlow, PyTorch, and Scikit\-learn.
- Advanced proficiency in Python and/or R for data engineering, automation, analytics, and machine learning development.
- Experience designing and maintaining ETL/ELT pipelines, data warehouses, and data lake/lakehouse environments.
- Experience with enterprise data visualization and analytics platforms, including Power BI, Tableau, Qlik, Palantir Foundry, or Maven Smart System (MSS).
- Experience working with distributed data processing technologies such as Apache Spark, Databricks, and Hadoop.
- Experience administering or utilizing relational database platforms including SQL Server, Oracle, PostgreSQL, and MySQL.
- Strong written and verbal communication skills with demonstrated experience briefing senior military or government leadership.
- Ability to travel occasionally to domestic and international locations.
Preferred Qualifications
- Master's degree in Data Science, Computer Science, Analytics, Information Technology, or another related technical discipline.
- Experience implementing enterprise data and analytics solutions within Azure, AWS, or Google Cloud environments.
- Strong knowledge of Department of Defense data strategies, governance frameworks, cybersecurity requirements, and information assurance standards.
- Experience with ESRI ArcGIS technologies and geospatial analytics.
- Demonstrated success leading cross\-functional technical teams in fast\-paced operational environments.
- Experience supporting AI/ML modernization initiatives within Department of Defense or Intelligence Community organizations.
Benefits
Venatôre offers a competitive benefits package designed to support the well\-being of our employees, including:
- Paid Time Off (PTO)
- 10 Federal Holidays
- 401(k) with company matching
- Medical, dental, and vision insurance
- Paid military leave
*Venatôre is a woman owned small business and an equal opportunity employer. We consider qualified applicants without regard to disability or protected veteran status.*
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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Venatore, 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 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Venatore AI Hiring
Venatore has 1 open AI role right now. They're hiring across Data Scientist. Based in Tampa, FL, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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