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Head Data Science Jobs in Florida (NOW HIRING)

IQVIA

Head of Product

Miami, FL · On-site

Position Overview The Head of Product - Media OS, IQVIA Digital will own the vision, strategy ... Collaborate with Engineering, Data Science, Design, Sales, Marketing, Operations, and Client ...

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IQVIA

Head of Product

Orlando, FL · On-site

Position Overview The Head of Product - Media OS, IQVIA Digital will own the vision, strategy ... Collaborate with Engineering, Data Science, Design, Sales, Marketing, Operations, and Client ...

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All JobsHead Data Science JobsHead Data Science Jobs in Florida

Head Data Science information

See Florida salary details

$17.9K

$87.7K

$162.5K

How much do head data science jobs pay per year?

As of Jul 13, 2026, the average yearly pay for head data science in Florida is $87,724.00, according to ZipRecruiter salary data. Most workers in this role earn between $47,795.00 and $118,692.00 per year, depending on experience, location, and employer.

How to become head of data science?

To become a head of data science, professionals typically need extensive experience in data analysis, machine learning, and leadership roles, often requiring 8-10 years in data-related positions. A strong educational background in computer science, statistics, or related fields, along with skills in programming, data management, and strategic planning, is essential. Advanced degrees and certifications in data science or analytics can also enhance prospects for leadership positions.

Is 40 too late for data science?

The Head Data Science role and similar data science positions do not have strict age limits; many professionals transition into data science later in their careers. Success depends on relevant skills, experience, and continuous learning in areas like programming, statistics, and machine learning, regardless of age.

What is the highest paid job in data science?

The highest paid roles in data science are often senior positions such as Chief Data Officer, Director of Data Science, or Lead Data Scientist, with salaries exceeding $150,000 annually and sometimes reaching over $200,000 for those with extensive experience, advanced skills in machine learning, and industry expertise. These roles typically require strong leadership, strategic thinking, and proficiency with tools like Python, R, and cloud platforms.

What is the 80 20 rule in data science?

In data science, the 80/20 rule, also known as Pareto principle, suggests that roughly 80% of results come from 20% of the efforts or features. Data scientists often use this concept to focus on the most impactful variables or tasks to optimize model performance and efficiency.

What does a Head of Data Science do?

A Head of Data Science is responsible for leading and managing the data science team within an organization. They oversee the development and implementation of data-driven strategies, ensuring that the team delivers valuable insights and predictive models to support business goals. This role involves collaborating with other departments, setting the vision for data initiatives, and ensuring best practices in data analysis and machine learning are followed. Additionally, the Head of Data Science often mentors team members and helps shape the organization's overall data strategy.

What are some common challenges faced by a Head of Data Science when building and leading a data science team?

As a Head of Data Science, one of the main challenges is balancing strategic leadership with hands-on technical guidance. You'll often need to align the team's goals with broader business objectives while ensuring that team members have the right mix of skills and resources. Additionally, fostering effective collaboration between data scientists, engineers, and business stakeholders can be complex, especially in cross-functional environments. Managing expectations around project timelines and communicating technical insights in a clear, actionable way are also key aspects of the role.

What are the key skills and qualifications needed to thrive as a Head of Data Science, and why are they important?

To thrive as a Head of Data Science, you need advanced expertise in statistics, machine learning, data modeling, and a strong background in computer science or a related quantitative field, often supported by a master's or Ph.D. Proficiency with programming languages like Python or R, big data platforms such as Hadoop or Spark, and familiarity with cloud-based analytics tools are typically required. Strategic leadership, excellent communication skills, and the ability to mentor and inspire teams are crucial soft skills for this role. These abilities are essential to drive data-driven decision-making, foster innovation, and align analytics initiatives with organizational goals.

What is the difference between Head Data Science vs Data Science Manager?

AspectHead Data ScienceData Science Manager
ResponsibilitiesStrategic leadership, setting data science vision, overseeing multiple teamsTeam management, project delivery, coordinating data science projects
Required SkillsAdvanced analytics, leadership, strategic planningTeam management, technical expertise, project management
ExperienceSenior data science background, leadership rolesData science experience with managerial responsibilities
Work EnvironmentExecutive level, cross-departmental collaborationTeam-focused, project-oriented

The Head Data Science typically holds a strategic, leadership role overseeing the entire data science function, while the Data Science Manager focuses on managing teams and project execution. Both roles require strong technical backgrounds, but the Head Data Science emphasizes vision and strategy, whereas the Data Science Manager concentrates on operational management.

What are the most commonly searched types of Data Science jobs in Florida? The most popular types of Data Science jobs in Florida are:
Head Data Science jobs near you
Infographic showing various Head Data Science job openings in Florida as of July 2026, with employment types broken down into 1% As Needed, 81% Full Time, 14% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $87,724 per year, or $42.2 per hour.
Head of Data Science

Head of Data Science

Octagon Talent

Fort Lauderdale, FL

Full-time

Posted 12 days ago

Job description

Octagon Talent Solutions is partnering with a fast-moving financial technology company that is building advanced machine learning products to detect fraud, strengthen identity verification, and support better real-time risk decisioning across financial services.


We are seeking a Head of Data Science to lead a growing team of full-stack data scientists responsible for developing production-grade models that identify fraudsters and expand the company’s suite of financial risk products. This is a high-impact leadership role for someone who combines strong applied machine learning expertise, deep business intuition, and the ability to mentor talented data scientists through complex, high-visibility work.


In this role, you will directly manage a team that starts at approximately 2–3 data scientists and grows to 5–6. You will serve as a technical leader, mentor, and domain owner across application fraud, helping the team build models and analytical systems that influence real-time decisions for partners. The right candidate will be energized by end-to-end ownership, rapid iteration, and the kind of deep domain understanding that creates durable competitive advantage.


Responsibilities


  • Lead, mentor, and directly manage a team of highly skilled full-stack data scientists focused on application fraud, financial risk, and identity verification products.
  • Provide hands-on technical direction across model development, analysis, experimentation, production code, monitoring, and fraud-focused decision systems.
  • Guide the team through the full machine learning model development lifecycle, including data acquisition decisions, labeling strategy, featurization, model training, experimentation, productionalization, and ongoing performance monitoring.
  • Partner closely with senior leadership, product, engineering, risk operations, marketing, and sales teams to align priorities, communicate progress, and deliver high-impact solutions on aggressive timelines.
  • Develop strong business intuition around fraud patterns, risk signals, user behavior, and partner needs, then translate that understanding into practical data science solutions.
  • Research emerging fraud behaviors and help create new products and capabilities around identity verification and application risk.
  • Drive success through rapid iteration, integration of new data sources, inventive feature engineering, and disciplined evaluation of model performance.
  • Write and review production-ready code used in real-time decision-making systems.
  • Design, perform, and present analyses that inform data acquisition, product development, risk operations priorities, marketing strategy, and sales efforts.
  • Challenge the team’s thinking, probe assumptions, and create an environment where data scientists consistently produce their best work.


Requirements


  • 7–15 years of experience in applied machine learning, data science, or a closely related technical field.
  • Proven experience building and deploying production machine learning models in fintech, cybersecurity, fraud detection, identity verification, risk, trust and safety, or another high-stakes domain.
  • Experience managing or mentoring high-performing data scientists, machine learning engineers, or analytically rigorous technical teams.
  • Strong hands-on technical ability across model development, statistical analysis, feature engineering, experimentation, and production-quality coding.
  • Ability to operate as both a people leader and technical leader, with the credibility to dive deep into details while also setting direction.
  • Strong business judgment and the ability to connect technical work to product outcomes, partner value, and operational priorities.
  • Experience working cross-functionally with engineering, product, senior leadership, and go-to-market teams.
  • Comfort operating in a fast-moving environment where timelines are aggressive, ambiguity is common, and domain insight is as important as methodology.
  • Excellent communication skills, including the ability to explain complex technical decisions and analytical findings to both technical and non-technical stakeholders.
  • Interest in fraud, financial risk, identity verification, and real-time decision systems.